Computer-aided cholelithiasis diagnosis using explainable convolutional neural network

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aided cholelithiasis diagnosis approaches have been introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models are black box in nature. Therefore, a novel approach for cholelithiasis classification using custom CNN with post-hoc model explanation is proposed. This paper presents multiple contributions. First, a custom CNN architecture is proposed to classify and predict cholelithiasis from ultrasound image. Second, a modified deep convolutional generative adversarial network is proposed to produce synthetic ultrasound images for better model generalization. Third, a hybrid visual explanation method is proposed by combining gradient-weighted class activation with local interpretable model agnostic explanation to generate a visual explanation using a heatmap. Fourth, an exhaustive performance analysis of the proposed approach on ultrasound images collected from three different Indian hospitals is presented to showcase its efficacy for computer-aided cholelithiasis diagnosis. Fifth, a team of radiologists evaluates and validates the prediction and respective visual explanations made using the proposed approach. The results reveal that the proposed cholelithiasis classification approach beats the performance of state-of-the-art pre-trained CNN and Vision Transformer models. The heatmap generated through the proposed hybrid explanation method offers detailed visual explanations to enhance transparency and trustworthiness in the medical domain.

Artificial Intelligence in Pancreatic Imaging: A Systematic Review

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning

Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. Pilot studies have demonstrated the potential for radiomic and artificial intelligence (AI) approaches in increasing diagnostic accuracy for the detection and classification of lymph nodes, but implementation of many of these approaches in real-world clinical settings would necessitate an automated lymph node segmentation pipeline as a first step. In this study, we aim to develop a non-invasive deep learning (DL) algorithm for detecting and automatically segmenting cervical lymph nodes in 25,119 CT slices from 221 normal neck contrast-enhanced CT scans from patients without head and neck cancer. We focused on the most challenging task of segmentation of small lymph nodes, evaluated multiple architectures, and employed U-Net and our adapted spatial context network to detect and segment small lymph nodes measuring 5-10 mm. The developed algorithm achieved a Dice score of 0.8084, indicating its effectiveness in detecting and segmenting cervical lymph nodes despite their small size. A segmentation framework successful in this task could represent an essential initial block for future algorithms aiming to evaluate small objects such as lymph nodes in different body parts, including small lymph nodes looking normal to the naked human eye but harboring early nodal metastases.

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

Purpose

Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs.

Methods and Materials

Ten patients, comprising 5 head and neck (HN) cancer patients and 5 prostate cancer patients, were randomly selected. Planning computed tomography images were subjected to autosegmentation using an Food and Drug Administration-approved AI software tool and manual segmentation by experienced radiation oncologists from 2 LMIC RT clinics. The control data, obtained from a large academic institution in the United States, consisted of contours obtained by an experienced radiation oncologist. The segmentation time, DICE similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated.

Results

AI significantly reduced segmentation time, averaging 2 minutes per patient, compared with 57 to 84 minutes for manual contouring in LMICs. Compared with the control data, the AI pelvic contours provided better agreement than did the LMIC manual contours (mean DSC of 0.834 vs 0.807 in LMIC1 and 0.844 vs 0.801 in LMIC2). For HN contours, AI provided better agreement for the majority of OAR contours than manual contours in LMIC1 (mean DSC: 0.823 vs 0.821) or LMIC2 (mean DSC: 0.792 vs 0.748). Neither the AI nor LMIC manual contours had good agreement with the control data (DSC < 0.600) for the optic nerves, chiasm, and cochlea.

Conclusions

AI-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.

Radiograph-based rheumatoid arthritis diagnosis via convolutional neural network

OBJECTIVES

Rheumatoid arthritis (RA) is a severe and common autoimmune disease. Conventional diagnostic methods are often subjective, error-prone, and repetitive works. There is an urgent need for a method to detect RA accurately. Therefore, this study aims to develop an automatic diagnostic system based on deep learning for recognizing and staging RA from radiographs to assist physicians in diagnosing RA quickly and accurately.

METHODS

We develop a CNN-based fully automated RA diagnostic model, exploring five popular CNN architectures on two clinical applications. The model is trained on a radiograph dataset containing 240 hand radiographs, of which 39 are normal and 201 are RA with five stages. For evaluation, we use 104 hand radiographs, of which 13 are normal and 91 RA with five stages.

RESULTS

The CNN model achieves good performance in RA diagnosis based on hand radiographs. For the RA recognition, all models achieve an AUC above 90% with a sensitivity over 98%. In particular, the AUC of the GoogLeNet-based model is 97.80%, and the sensitivity is 100.0%. For the RA staging, all models achieve over 77% AUC with a sensitivity over 80%. Specifically, the VGG16-based model achieves 83.36% AUC with 92.67% sensitivity.

CONCLUSION

The presented GoogLeNet-based model and VGG16-based model have the best AUC and sensitivity for RA recognition and staging, respectively. The experimental results demonstrate the feasibility and applicability of CNN in radiograph-based RA diagnosis. Therefore, this model has important clinical significance, especially for resource-limited areas and inexperienced physicians.

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation

BACKGROUND AND OBJECTIVE

Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans.

METHODS

Specifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture.

RESULTS

Through extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas.

CONCLUSIONS

The devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning including unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemmas in multi-organ segmentation. We first review the fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

MCNet: A multi-level context-aware network for the segmentation of adrenal gland in CT images

Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, the small size of the adrenal gland, which occupies less than 1% of the abdominal CT slice, poses a significant challenge to accurate segmentation. To address this problem, we propose a novel multi-level context-aware network (MCNet) to segment adrenal glands in CT images. Our MCNet mainly consists of two components, i.e., the multi-level context aggregation (MCA) module and multi-level context guidance (MCG) module. Specifically, the MCA module employs multi-branch dilated convolutional layers to capture geometric information, which enables handling of changes in complex scenarios such as variations in the size and shape of objects. The MCG module, on the other hand, gathers valuable features from the shallow layer and leverages the complete utilization of feature information at different resolutions in various codec stages. Finally, we evaluate the performance of the MCNet on two CT datasets, including our clinical dataset (Ad-Seg) and a publicly available dataset known as Distorted Golden Standards (DGS), from different perspectives. Compared to ten other state-of-the-art segmentation methods, our MCNet achieves 71.34% and 75.29% of the best Dice similarity coefficient on the two datasets, respectively, which is at least 2.46% and 1.19% higher than other segmentation methods.

A combined encoder-transformer-decoder network for volumetric segmentation of adrenal tumors

BACKGROUND

The morphology of the adrenal tumor and the clinical statistics of the adrenal tumor area are two crucial diagnostic and differential diagnostic features, indicating precise tumor segmentation is essential. Therefore, we build a CT image segmentation method based on an encoder-decoder structure combined with a Transformer for volumetric segmentation of adrenal tumors.

METHODS

This study included a total of 182 patients with adrenal metastases, and an adrenal tumor volumetric segmentation method combining encoder-decoder structure and Transformer was constructed. The Dice Score coefficient (DSC), Hausdorff distance, Intersection over union (IOU), Average surface distance (ASD) and Mean average error (MAE) were calculated to evaluate the performance of the segmentation method.

RESULTS

Analyses were made among our proposed method and other CNN-based and transformer-based methods. The results showed excellent segmentation performance, with a mean DSC of 0.858, a mean Hausdorff distance of 10.996, a mean IOU of 0.814, a mean MAE of 0.0005, and a mean ASD of 0.509. The boxplot of all test samples' segmentation performance implies that the proposed method has the lowest skewness and the highest average prediction performance.

CONCLUSIONS

Our proposed method can directly generate 3D lesion maps and showed excellent segmentation performance. The comparison of segmentation metrics and visualization results showed that our proposed method performed very well in the segmentation.

Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typically excel at capturing fine local features. Furthermore, there is a wealth of complementary information between their segmentation predictions. Inspired by this observation, we develop an Uncertainty-aware Multi-dimensional Mutual learning framework to learn different dimensional networks simultaneously, each of which provides useful soft labels as supervision to the others, thus effectively improving the generalization ability. Specifically, our framework builds upon a 2D-CNN, a 2.5D-CNN, and a 3D-CNN, while an uncertainty gating mechanism is leveraged to facilitate the selection of qualified soft labels, so as to ensure the reliability of shared information. The proposed method is a general framework and can be applied to varying backbones. The experimental results on three datasets demonstrate that our method can significantly enhance the performance of the backbone network by notable margins, achieving a Dice metric improvement of 2.8% on MeniSeg, 1.4% on IBSR, and 1.3% on BraTS2020.

Remote Health Monitoring Systems for Elderly People: A Survey

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

Machine learning-based Decision Tree J48 with grey wolf optimizer for environmental pollution control

ABSTRACTDue to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health issues like respiratory and cardiac problems. The major air pollutants are NO, C₆H₆, CO, etc. Many research works have been done in predicting air pollution-based health issues, predicting air pollution levels, monitoring and controlling the polluted levels. But they are not efficient, cost of maintenance is high and insufficient tool for monitoring it. To overcome these issues, this paper implements hybrid algorithm of Decision Tree J48 and Grey Wolf Optimizer (DT-GWO). This DT-GWO is a better model to addresses the predicting of Air Quality Index (AQI), which minimizes the error rate, accurately and effectively predicting the air quality. The AQI values are categorised as good, moderate, unhealthy, very unhealthy and hazardous. The dataset used in this work is collected from Kaggle website which contains air pollutants details with air quality index values. Accuracy obtained for decision Tree J48 is 93.72%, grey wolf optimizer is 96.83% and our proposed work DT-GWO is 99.78%.

Boundary-Sensitive Loss Function With Location Constraint for Hard Region Segmentation

In computer-aided diagnosis and treatment planning, accurate segmentation of medical images plays an essential role, especially for some hard regions including boundaries, small objects and background interference. However, existing segmentation loss functions including distribution-, region- and boundary-based losses cannot achieve satisfactory performances on these hard regions. In this paper, a boundary-sensitive loss function with location constraint is proposed for hard region segmentation in medical images, which provides three advantages: i) our Boundary-Sensitive loss (BS-loss) can automatically pay more attention to the hard-to-segment boundaries (e.g., thin structures and blurred boundaries), thus obtaining finer object boundaries; ii) BS-loss also can adjust its attention to small objects during training to segment them more accurately; and iii) our location constraint can alleviate the negative impact of the background interference, through the distribution matching of pixels between prediction and Ground Truth (GT) along each axis. By resorting to the proposed BS-loss and location constraint, the hard regions in both foreground and background are considered. Experimental results on three public datasets demonstrate the superiority of our method. Specifically, compared to the second-best method tested in this study, our method improves performance on hard regions in terms of Dice similarity coefficient (DSC) and 95% Hausdorff distance (95%HD) of up to 4.17% and 73% respectively. In addition, it also achieves the best overall segmentation performance. Hence, we can conclude that our method can accurately segment these hard regions and improve the overall segmentation performance in medical images.

DNA Methylation Analysis of the SHOX2 and RASSF1A Panel Using Cell-Free DNA in the Diagnosis of Malignant Pleural Effusion

Objectives

The differential diagnosis of pleural effusion (PE) is a common but major challenge in clinical practice. This study aimed to establish a strategy based on a PE-cell-free DNA (cfDNA) methylation detection system for the differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE).

Methods

A total of 104 patients with PE were enrolled in this study, among which 50 patients had MPE, 9 malignant tumor patients had PE of indefinite causes, and the other 45 patients were classified as benign controls. The methylation status of short stature homeobox 2 (SHOX2) and RAS association domain family 1, isoform A (RASSF1A) was detected using PE-cfDNA specimens by real-time fluorescence quantitative PCR. Total methylation (TM) was defined as the combination of the methylation levels of SHOX2 and RASSF1A. The electrochemiluminescence immunoassay was applied to evaluate the levels of multiple serum tumor markers.

Results

The PE-cfDNA methylation status of either SHOX2 or RASSF1A was much higher in MPE samples than in benign controls. The combination of SHOX2 and RASSF1A methylation in PE yielded a diagnostic sensitivity of 96% and a specificity of 100%, respectively. When compared with the corresponding serum tumor marker detection results, TM showed the highest diagnostic efficiency (AUC = 0.985). Furthermore, the combination of the SHOX2 and RASSF1A methylation panels using PE-cfDNA could apparently improve the differential diagnostic efficacy of BPE and MPE and could help compensate for the deficiency of cytology.

Conclusions

Our results indicated that SHOX2 and RASSF1A methylation panel detection could accurately classify BPE and MPE diseases and showed better diagnostic performance than traditional serum parameters. The SHOX2 and RASSF1A methylation detection of PE-cfDNA could be a potentially effective complementary tool for cytology in the process of differential diagnosis. In summary, PE-cfDNA could be used as a promising non-invasive analyte for the auxiliary diagnosis of MPE.

MaskID: An effective deep-learning-based algorithm for dense rebar counting

As a dense instance segmentation problem, rebar counting in a complex environment such as rebar yard and rebar transpotation has received significant attention in both academic and industrial contexts. Traditional counting approaches, such as manual counting and machine vision-based algorithms, are often inefficient or inaccurate since rebars with varied sizes and shapes are stacked overlapping, rebar image is not clear for complex light condition such as dawn, night and strong light, and other environmental noises exist in rebar image; thus, they no longer fulfil the requirements of modern automation. This paper proposes MaskID, an innovative counting method based on deep learning and heuristic strategies. First, an improved version of the Mask region-based convolutional neural network (Mask R-CNN) was designed to obtain the segmentation results through splitting and rescaling so as to capture more detail in a large-scale rebar image. Then, a series of intelligent denoising strategies corresponding to aspect ratio of recognized box, overlapping recognized objects, object distribution and environmental noise, were applied to improve the segmentation results. The performance of the proposed method was evaluated on open-competition and test-platform datasets. The F1-score was found to be over 0.99 on all datasets. The experimental results demonstrate that the proposed method is effective for dense rebar counting and significantly outperforms existing state-of-the-art methods.

Hierarchical volumetric transformer with comprehensive attention for medical image segmentation

Transformer is widely used in medical image segmentation tasks due to its powerful ability to model global dependencies. However, most of the existing transformer-based methods are two-dimensional networks, which are only suitable for processing two-dimensional slices and ignore the linguistic association between different slices of the original volume image blocks. To solve this problem, we propose a novel segmentation framework by deeply exploring the respective characteristic of convolution, comprehensive attention mechanism, and transformer, and assembling them hierarchically to fully exploit their complementary advantages. Specifically, we first propose a novel volumetric transformer block to help extract features serially in the encoder and restore the feature map resolution to the original level in parallel in the decoder. It can not only obtain the information of the plane, but also make full use of the correlation information between different slices. Then the local multi-channel attention block is proposed to adaptively enhance the effective features of the encoder branch at the channel level, while suppressing the invalid features. Finally, the global multi-scale attention block with deep supervision is introduced to adaptively extract valid information at different scale levels while filtering out useless information. Extensive experiments demonstrate that our proposed method achieves promising performance on multi-organ CT and cardiac MR image segmentation.

SPORTS REHABILITATION IN ATHLETES WITH MENISCAL LESIONS BASED ON ELECTROACUPUNCTURE ASSOCIATED WITH SPORTS THERAPY

ABSTRACT Introduction: Meniscal injury is a common condition that can lead to disability due to pain and proprioceptive failure, requiring immediate attention. Combination therapies involve advanced approaches aiming to accelerate rehabilitation in athletes, and electroacupuncture presents therapeutic benefits, although there is still no evidence of its combination with sports therapy. Objective: This paper analyzes the performance of sports rehabilitation in athletes with meniscal lesions using electroacupuncture combined with sports therapy. Methods: The intervention in the control group was based on a traditional range of motion work, muscle strength, proprioceptive training, and other exercise therapies, while the experimental group received a 30 min electro-acupuncture protocol three times a week for four consecutive weeks. The surrogate data (gender, age, disease course, location) are the same. Before treatment, joint activity, muscle strength, total joint scale score of the LYSHOLM questionnaire, and other observational indices were measured during the 6th and 12th week of treatment. The non-parametric statistical method and T-test were used to analyze the changes of each index before and after treatment. After 12 weeks of treatment, the difference between the experimental group and the combination before treatment was significant. Results: The treatment effect of the experimental group was significantly better than the control group. Conclusion: The effect of sports rehabilitation of athletes with meniscus injury based on electroacupuncture combined with sports therapy showed high resolutive application value, indicating an alternative for non-surgical treatment in knee meniscus injuries. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Enhanced You Only Look Once X for surface defect detection of strip steel

Using deep learning-based methods to detect surface defects in strip steel can reduce the impact of human factors and lower costs while maintaining accuracy and efficiency. However, the main disadvantages of this method is the inability to tradeoff accuracy and efficiency. In addition, the low proportion of valid information and the lack of distinctive features result in a high rate of missed detection of small objects. In this paper, we propose a lightweight YOLOX surface defect detection network and introduce the Multi-scale Feature Fusion Attention Module (MFFAM). Lightweight CSP structures are used to optimize the backbone of the original network. MFFAM uses different scales of receptive fields for feature maps of different resolutions, after which features are fused and passed into the spatial and channel attention modules in parallel. Experimental results show that lightweight CSP structures can improve the detection frame rate without compromising accuracy. MFFAM can significantly improve the detection accuracy of small objects. Compared with the initial YOLOX, the mAP and FPS were 81.21% and 82.87Hz, respectively, which was an improvement of 4.29% and 12.72Hz. Compared with existing methods, the proposed model has superior performance and practicality, verifying the effectiveness of the optimization method.

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring

BACKGROUND

Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end-to-end deep learning (DL) networks, are weak in garnering high-level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge.

PURPOSE

We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation.

METHODS

The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI-based automatic anatomy recognition object recognition (AAR-R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL-based recognition (DL-R), which refines the coarse recognition results of AAR-R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR-R fuzzy model of each object guided by the BBs output by DL-R; and (v) DL-based delineation (DL-D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system.

RESULTS

The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground-truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53 mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto-contours and clinically drawn contours.

CONCLUSIONS

The HI system is observed to behave like an expert human in robustness in the contouring task but vastly more efficiently. It seems to use NI help where image information alone will not suffice to decide, first for the correct localization of the object and then for the precise delineation of the boundary.

Transfer learning: a friendly introduction

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Transfer learning: a friendly introduction

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm

With the increasingly important role of image segmentation in the field of computed tomography (CT) image segmentation, the requirements for image segmentation technology in related industries are constantly improving. When the hardware resources can fully meet the needs of the fast and high-precision image segmentation program system, the main means of how to improve the image segmentation effect is to improve the related algorithms. Therefore, this study has proposed a combination of genetic algorithm (GA) and Great Law (OTSU) algorithm to form an image segmentation algorithm-immune genetic algorithm (IGA) algorithm. The algorithm has improved the segmentation accuracy and efficiency of the original algorithm, which is beneficial to the more accurate results of CT image segmentation. The experimental results in this study have shown that the operating efficiency of the OTSU segmentation algorithm is up to 75%. The operating efficiency of the GA algorithm is up to 78%. The operating efficiency of the IGA algorithm is up to 92%. In terms of operating efficiency, the OTSU segmentation algorithm has more advantages. In terms of segmentation accuracy, the highest accuracy rate of OTSU segmentation algorithm is 45%. The accuracy of the GA algorithm is 80%. The highest accuracy of the IGA algorithm is 97%. The IGA algorithm is more powerful in terms of operating efficiency and accuracy. Therefore, the application of the IGA algorithm to CT image segmentation is beneficial to doctors to better judge the lesions and improve the diagnosis rate.

Novel deep learning architectures for haemodialysis time series classification

Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. In particular, we have defined two novel architectures, able to take advantage of the strengths of Convolutional Neural Networks and of Recurrent Networks. The novel architectures we introduced and tested outperformed classical mathematical classification techniques, as well as simpler deep learning approaches. In particular, combining Recurrent Networks with convolutional structures in different ways, allowed us to obtain accuracies above 81%, coupled with high values of the Matthews Correlation Coefficient (MCC), a parameter particularly suitable to assess the quality of classification when dealing with unbalanced classes-as it was our case. In the future we will test an extension of the approach to additional monitoring time series, aiming at an overall optimization of patient care.

Global and Local Feature Reconstruction for Medical Image Segmentation

Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate these two problems, but the global feature extraction ability and spatial information recovery ability of U-Net are still insufficient. In this paper, we propose a Global Feature Reconstruction (GFR) module to efficiently capture global context features and a Local Feature Reconstruction (LFR) module to dynamically up-sample features, respectively. For the GFR module, we first extract the global features with category representation from the feature map, then use the different level global features to reconstruct features at each location. The GFR module establishes a connection for each pair of feature elements in the entire space from a global perspective and transfers semantic information from the deep layers to the shallow layers. For the LFR module, we use low-level feature maps to guide the up-sampling process of high-level feature maps. Specifically, we use local neighborhoods to reconstruct features to achieve the transfer of spatial information. Based on the encoder-decoder architecture, we propose a Global and Local Feature Reconstruction Network (GLFRNet), in which the GFR modules are applied as skip connections and the LFR modules constitute the decoder path. The proposed GLFRNet is applied to four different medical image segmentation tasks and achieves state-of-the-art performance.

Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology

Liver cirrhosis is a clinical chronic developmental liver disease, which is caused by long-term or repeated effects of liver dysfunction, and there are more and more cases of epileptic seizures in patients with liver cirrhosis and HEV infection. This article aims to study how to analyze epileptic seizures in patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology. This article proposes a deep learning neural network algorithm based on deep multimodal fusion technology, and how to use this algorithm to automatically detect and classify epileptic seizures. The data in the experiment in this article show that the prevalence of epilepsy accounts for 1% of the world's population, about 56.7 million people, and 1 in 25 people may have an epileptic seizure at some time in their lives, and in each person's life, the probability of seizures due to various reasons is 10%. In 2016, the proportion of males with cirrhosis reached 16%, females reached 8%, and males were 8% higher than females, which is a full double. The test results show that with the increase in patients with cirrhosis and overlapping HEV infection, the frequency of epileptic seizures is also getting higher and higher, indicating that the frequency of epileptic seizures has been increased in patients with cirrhosis and overlapping HEV infection. Therefore, it is imperative to analyze the epileptic seizures of patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology.

Regulation of Quality of Life and Immune Function in Patients with Thyroid Cancer Treated by Deep Learning Technology

Background

In order to explore the regulation of quality of life and immune function in patients with thyroid cancer after radiotherapy, a method based on deep learning technology was proposed. A deep learning detection method for thyroid cancer is proposed.

Methods

It mainly includes three main modules: data preprocessing, thyroid cancer regional detection module, and thyroid cancer benign and malignant classification module. The data set in the experiment comes from LIDC-IDRI and is processed by the data preprocessing module to generate a standard data format that can be processed by the framework. The treatment of thyroid cancer can help patients relapse malignant thyroid cancer and prevent recurrence in advance.

Results

The results showed that most patients are diagnosed because of obvious swelling of local thyroid mass and conscious compression symptoms in the neck. At this time, they often miss the best treatment time, so as to reduce the surgical effect.

Conclusions

The metastasis and invasion of cancer cells are fast, the cancerous lesions are easy to form adhesion with the surrounding tracheal tissue, and the cancer cells invade the surrounding soft tissue, which is also easy to cause the cancerous tissue not to be completely removed. Clinical Trial Registration. Therefore, deep learning technology is used to treat residual cancerous lesions to ensure the surgical effect.

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

An in-depth learning-based approach is designed to develop the ability to recognize human behavior on the move. We introduce 3D residual structures and create 3D residual models. In order to get the most out of the data relationship of several consecutive frames, this study introduces 3D techniques for assigning different values to the existing frames. Experiments show that both structures improve recognition performance. For the 3D residual model, 3D attention model, and 3D attention residual model, this study proposes two model fusion strategies: average and weighted. Among them, the weighted fusion is to give a higher fusion proportion to the high accuracy model by using the model weight calculation method designed in this study. The experimental results show that the additive fusion strategy based on feature contribution has an obvious improvement effect on the test results of the two benchmark datasets, with an increase of more than 2% points, including an increase of 2.69% on HMDB51. The effect of splicing and fusion strategy has also increased by more than 1% point, including 1.34% on UCF101 dataset and about 1.9% on HMDB51. It is proven that deep learning can effectively recognize human behavior in sports.

Mathematical model and genomics construction of developmental biology patterns using digital image technology

Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today’s microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.

Design and Implementation of Local Threshold Segmentation Based on FPGA

In the process of the development of image processing technology, image segmentation is a very important image processing technology in the field of machine vision, pedestrian detection, medical imaging, and so on. However, the traditional image segmentation technology cannot solve the problems of reflection and uneven illumination. This paper presents a local threshold segmentation method based on FPGA, which can automatically select the optimal threshold according to different gray levels of images. First, the image is processed by mean filtering to remove noise interference in the image. Then, the idea of the mean value of the local neighborhood block and the Gaussian weighted sum in the local neighborhood is used to deal with the reflective and uneven light on the image. The process is designed and realized on FPGA. Finally, the design algorithm is verified by ModelSim simulation software and QT5 software. The experimental results show that the algorithm can effectively solve the problems of reflection and uneven illumination on the image surface, and the segmentation effect is significantly improved compared with the fixed threshold algorithm and Otsu algorithm. It also has certain reference value in medicine, agriculture, engineering, and other fields.

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors’ clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients’ physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician’s diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.

Construction of Economic Data Management System Based on BP Neural Network

In order to further understand the economic data management system and technology, in-depth research was conducted in the state of people's nervous system feeling. The method of building open platform algorithm to optimize and modify weight rule 2BP grid construction was used to study. According to the basic principle, the BP neural network which is more suitable for economic data management system was constructed. At the same time, to construct economic database resources, neural network system was mainly to simplify and abstract or simulate the human brain nervous system, which is not completely the same, but can also map the basic characteristics of many functions of the human brain. Through the analysis of the economic data of the neural network, the neural network is widely used in the economic data management, which not only improves the management level of enterprises, but also improves the benefits and profits of enterprises. Besides, it has application effect in predicting economic early warning risk analysis cost control strategy management enterprise credit evaluation and enterprise competitiveness evaluation.

A Unified 3D Framework for Organs-at-Risk Localization and Segmentation for Radiation Therapy Planning

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260 ± 0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.

An Optimized U-Net for Unbalanced Multi-Organ Segmentation

Medical practice is shifting towards the automation and standardization of the most repetitive procedures to speed up the time-to-diagnosis. Semantic segmentation repre-sents a critical stage in identifying a broad spectrum of regions of interest within medical images. Indeed, it identifies relevant objects by attributing to each image pixels a value representing pre-determined classes. Despite the relative ease of visually locating organs in the human body, automated multi-organ segmentation is hindered by the variety of shapes and dimensions of organs and computational resources. Within this context, we propose BIONET, a U-Net-based Fully Convolutional Net-work for efficiently semantically segmenting abdominal organs. BIONET deals with unbalanced data distribution related to the physiological conformation of the considered organs, reaching good accuracy for variable organs dimension with low variance, and a Weighted Global Dice Score score of 93.74 ± 1.1%, and an inference performance of 138 frames per second. Clinical Relevance - This work established a starting point for developing an automatic tool for semantic segmentation of variable-sized organs within the abdomen, reaching considerable accuracy on small and large organs with low variability, reaching a 93.74 ± 1.1 % of Weighted Global Dice Score.

Wushu Routine Movement and Diagnosis Based on Deep Learning and Symmetric Difference Algorithm

Wushu is one of the traditional cultural symbols of the Chinese nation. It is also one of the most popular sports activities among the people. With the attention and love of contemporary people to sports activities, Wushu is also constantly developing and innovating. The requirements for professional martial arts routines of martial arts athletes are higher than ever. The development of martial arts has also made martial arts competitions more intense, and often a small detail of martial arts movements can determine the success or failure of the competition. Therefore, various Wushu teams pay more and more attention to the analysis and diagnosis of Wushu routines. It ensures that coaches and athletes can obtain more quantitative indicators of technical movement training. The analysis and diagnosis of martial arts routines are inseparable from the support of reliable science and technology and related algorithms. This article aims to study the analysis and diagnosis of martial arts routines based on deep learning and symmetric difference algorithm. It combines deep learning and symmetric difference algorithm to analyze and diagnose martial arts routines. The article concludes that the level of martial arts routines of martial arts athletes has the greatest influence on their martial arts competition performance, and its comprehensive influence index is as high as 4.3.

Effect of Repairing Tendon and Ligament Injury of Wushu Athletes by Medical Image

Medical imaging can be used as a medical aid for diagnosis and treatment, and color Doppler ultrasound can also be used in life science research as a scientific research method. Wushu is a traditional sport in China, which has a long history of development. Martial arts are a very good fitness project, but different from ordinary people, professional martial arts athletes are often accompanied by a variety of sports injuries, and tendon ligament injury is one of the most common injuries. At present, there are many treatment plans for tendon and ligament injury, but there are few researches on the repair effect of tendon and ligament injury. This paper will take this as the main research purpose for in-depth study. In view of the problem that ligament injury is not easy to observe, this paper will use GE Lightspeed 64 row spiral CT as the main observation tool and use the method of hospital image observation to compare and analyze the repair effect of tendon and ligament injury of Wushu athletes. In this experiment, 88 professional Wushu athletes were gathered as experimental samples. After preliminary screening, 110 cases of ligament injury were counted. After analyzing the abnormal changes of tissue or structure, Lysholm, and IKDC treatment effect score data, this paper believes that, for type I patients, only conservative treatment can achieve good results. However, in the more serious and complex type II patients, local fixation is used after the onset of the disease, and very serious patients can achieve good results through surgical treatment. Postoperative care is also important, which helps reduce complications. This experiment has achieved ideal results and has played a blank role in the research of the repair effect of tendon and ligament injury of Wushu athletes at home and abroad.

Conservative Treatment and Rehabilitation Training for Rectus Femoris Tear in Basketball Training Based on Computer Vision

Computer vision is an emerging artificial intelligence subject, whose purpose is to make computers have the same ability to perceive and understand image semantic information as humans. Computer vision technology is based on high-performance computers, which can obtain massive amounts of information and data in a short period of time and use intelligent algorithms to perform high-speed data processing on the information, which is conducive to the integration of information related to product design, production process management, etc. Due to the rapid development of visual sensing technology, computer technology, and image processing technology, computer vision technology has been widely used in the fields of food, medicine, construction, chemical industry, electronics, packaging, and automobiles. This article uses computer vision technology to compare four conservative treatments and rehabilitation training for rectus femoris in basketball training and analyze the best rehabilitation treatment for rectus femoris tear. The experimental results show that the average electroacupuncture plus muscle stretching exercise group after treatment has an average EMG value of 55.49, an average muscle strength rating of five, an average motor function score of 23.45, and an average treatment recovery time of 11.6 days. This group has the best treatment effect.

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.

Evaluation and Correlation Analysis of Mental and Psychological Factors and Premature Ejaculation in Patients with Benign Prostatic Hyperplasia in Mobile Medical System

Chronic prostatitis is a very common and very difficult disease. Based on the mobile medical system, this paper carried out a correlation analysis on the psychological factors and the evaluation of premature ejaculation in patients with benign prostatic hyperplasia. The article first analyzes the application in the field of mobile medical and then introduces the prostate image segmentation method based on the geometric active contour model. The emergence of automatic organ tissue segmentation technology is timely; it can help clinicians save a lot of manual segmentation time and has better reversibility and objectivity, and the theory of curve evolution is analyzed. Finally, this paper introduces the experimental research object and purpose, makes a statistical analysis of the symptoms of benign prostatic hyperplasia, and compares the incidence of psychological symptoms in patients with prostatitis under different factors. The experimental results of this paper show that 90% of prostatitis patients have mild psychological problems, and 10% have moderate psychological problems. Among them, the main reason for the psychological disorder of patients is depression, for which we should strengthen the care for patients.

Influence of Health Education Based on IMB on Prognosis and Self-Management Behavior of Patients with Chronic Heart Failure

As the contemporary society is increasingly entering an aging society, heart failure, as a common disease in the elderly population, has an increasing impact on people. The common one is mainly chronic heart failure. Coupled with the influence of various complications, such as hypostatic pneumonia and venous thrombosis, the mortality and hospital admission rates of patients are very high. Moreover, the current technology is not very effective for the prevention and treatment of chronic heart failure. The per capita consumption level of ordinary people in China is low, and it is not suitable to promote high-cost treatment programs. Based on this, this paper proposes the intervention management of mental failure patients under the intervention of health education based on IMB, in order to explore the impact of the intervention of health education on patients. The research in this paper selected 112 patients with chronic heart failure who were admitted to the cardiovascular ward of a city public hospital in 2017 and divided the patients into two groups. One group received health education intervention, which was the intervention group. The other group was the control group. The control group was given routine education and nursing. The experimental results of this paper show that the satisfaction of the intervention group is higher, accounting for 85.3%, and the satisfaction of the control group is lower than that of the intervention group, about 67.9%. Dissatisfaction with health education and the probability of short-term readmission were higher than those in the intervention group.

The Analysis of Sharing Economy on New Business Model Based on BP Neural Network

The development of social economy and Internet information technology has made the development of the sharing economy relatively rapid. This article aims to study how to promote the sharing economy based on neural networks to play a role in new business models. This article proposes that the sharing economy and the new business model are inseparable. It also discusses how to analyze the relationship between the sharing economy and the new business model based on the BP neural network. With the development of the economy and society, new economic development models have developed, and the sharing economy model has risen. The sharing economy model has brought an impact to the traditional economic development model, affecting the business model. The results show that with the development of society and enterprises, the development of the sharing economy is getting faster and faster. Today, some sharing economy companies are bound to face various obstacles in the process of copying other business models and development. Sharing economy enterprises have made various adjustments and responses to various problems, but they have not found a better model to adapt to the modern social market and environment. Therefore, the business model of the sharing economy requires further analysis and investigation.

Machine Vision and Intelligent Algorithm Based on Neural Network

Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

Evaluation of Cardiac Space-Occupying Lesions by Myocardial Contrast Echocardiography and Transesophageal Echocardiography

Heart space-occupying lesions are a disease that occurs frequently in clinical setting, and therefore, it is important to diagnose and treat this type of pathologies properly. Angiographic echocardiography and transesophageal sonogram are widely used for clinical diagnosis. Their application provides a guarantee for the diagnosis of cardiac space-occupying lesions. In this paper, the application of cardiac contrast echocardiography and transesophageal echocardiography in cardiac space-occupying lesions was studied. Prediction of cardiac lesions can accurately determine the nature of cardiac occupancies and provide a basis for clinical diagnosis and management judgments. The results of pathological analysis and experimental comparison showed that myocardial contrast echocardiography can accurately distinguish tumor and thrombus and make contribution to patients taking appropriate medical measures. At the same time, it can compare conventional transthoracic echocardiography and transesophageal echocardiography. The results showed that TEE could clearly show the cardiac lesions. The experimental data of 76.9% confirmed cases showed that the diagnostic accuracy is greatly improved. TEE can also clearly show small thrombus that TTE cannot, in which 2DTEE can clearly show the boundary between the space-occupying and surrounding tissues, and whether there is a clear boundary between the space-occupying and surrounding tissues is an important distinguishing point of benign and malignant tumors. In addition, the TEE probe can also be used for large angle imaging and multiangle rotation, so as to determine the tumor boundary and the spatial position relationship between the tumor and the surrounding tissue. All in all, myocardial contrast echocardiography and transesophageal echocardiography have better clinical application effect on cardiac space-occupying lesions.

Quetiapine Combined with Sodium Valproate in Patients with Alzheimer's Disease with Mental and Behavioral Symptoms Efficacy Observation

Quetiapine combined with sodium valproate is an effective and more suitable drug treatment for Alzheimer's disease. At present, there are relatively few studies on the combined action mechanism of these two drugs. This study has certain practical value. Alzheimer's disease is a multifaceted, highly genetically heterogeneous neurodegenerative disease. The main clinical manifestations are memory loss, abnormal mental behavior, and loss of various cognitive functions. In order to improve the symptoms of patients with Alzheimer's disease, especially those with mental symptoms, this article combines quetiapine and sodium valproate, two commonly used drugs for the treatment of mental illnesses, and applies them to different levels of Alzheimer's and observes the results of the combination's curative effect. This article introduces Alzheimer's disease and its potential mental behaviors in the method section, and it also introduces the mechanism of action of quetiapine and sodium valproate. For the algorithm, this paper introduces a data mining algorithm to understand the effect of drug efficacy. In the experimental part, firstly, it introduces the experimental objects, the proportion of medicines, and the statistical methods. Secondly, this article covers adverse reactions, inflammatory factors and vascular endothelial indicators, Alzheimer's disease performance, MOAS score, treatment effect evaluation, and satisfaction surveys. It can be seen from the experiment that, in mental behavior, the experimental group decreased from 8.2 before treatment to 0.5, and the control group decreased from 7.1 before treatment to 2.6. It can be seen that the scores of the experimental group changed after receiving the treatment of quetiapine combined with sodium valproate.

AI-driven deep convolutional neural networks for chest X-ray pathology identification

BACKGROUND

Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images.

OBJECTIVE

To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays.

METHOD

Several CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images.

RESULTS

In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes.

CONCLUSION

This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images.