A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Dual-branch dynamic hierarchical U-Net with multi-layer space fusion attention for medical image segmentation

Accurate segmentation of organs or lesions from medical images is essential for accurate disease diagnosis and organ morphometrics. Previously, most researchers mainly added feature extraction modules and simply aggregated the semantic features to U-Net network to improve the segmentation accuracy of medical images. However, these improved U-Net networks ignore the semantic differences of different organs in medical images and lack the fusion of high-level semantic features and low-level semantic features, which will lead to blurred or miss boundaries between similar organs and diseased areas. To solve this problem, we propose Dual-branch dynamic hierarchical U-Net with multi-layer space fusion attention (D2HU-Net). Firstly, we propose a multi-layer spatial attention fusion module, which makes the shallow decoding path provide predictive graph supplement to the deep decoding path. Under the guidance of higher semantic features, useful context features are selected from lower semantic features to obtain deeper useful spatial information, which makes up for the semantic differences between organs in different medical images. Secondly, we propose a dynamic multi-scale layered module that enhances the multi-scale representation of the network at a finer granularity level and selectively refines single-scale features. Finally, the network provides guiding optimization for subsequent decoding based on multi-scale loss functions. The experimental results on four medical data sets show D2HU-Net enables the most advanced segmentation capabilities on different medical image datasets, which can help doctors diagnose and treat diseases.

(KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network

The categorization of benign and malignant patterns in digital mammography is a critical step in the diagnosis of breast cancer, facilitating early detection and potentially saving many lives. Diverse breast tissue architectures often obscure and conceal breast issues. Classifying worrying regions (benign and malignant patterns) in digital mammograms is a significant challenge for radiologists. Even for specialists, the first visual indicators are nuanced and irregular, complicating identification. Therefore, radiologists want an advanced classifier to assist in identifying breast cancer and categorizing regions of concern. This study presents an enhanced technique for the classification of breast cancer using mammography images. The collection comprises real-world data from King Abdullah University Hospital (KAUH) at Jordan University of Science and Technology, consisting of 7,205 photographs from 5,000 patients aged 18-75. After being classified as benign or malignant, the pictures underwent preprocessing by rescaling, normalization, and augmentation. Multi-fusion approaches, such as high-boost filtering and contrast-limited adaptive histogram equalization (CLAHE), were used to improve picture quality. We created a unique Residual Depth-wise Network (RDN) to enhance the precision of breast cancer detection. The suggested RDN model was compared with many prominent models, including MobileNetV2, VGG16, VGG19, ResNet50, InceptionV3, Xception, and DenseNet121. The RDN model exhibited superior performance, achieving an accuracy of 97.82%, precision of 96.55%, recall of 99.19%, specificity of 96.45%, F1 score of 97.85%, and validation accuracy of 96.20%. The findings indicate that the proposed RDN model is an excellent instrument for early diagnosis using mammography images and significantly improves breast cancer detection when integrated with multi-fusion and efficient preprocessing approaches.

Detection of Masses in Mammogram Images Based on the Enhanced RetinaNet Network With INbreast Dataset

Purpose

Breast cancer is the most common major public health problems of women in the world. Until now, analyzing mammogram images is still the main method used by doctors to diagnose and detect breast cancers. However, this process usually depends on the experience of radiologists and is always very time consuming.

Patients and Methods

We propose to introduce deep learning technology into the process for the facilitation of computer-aided diagnosis (CAD), and address the challenges of class imbalance, enhance the detection of small masses and multiple targets, and reduce false positives and negatives in mammogram analysis. Therefore, we adopted and enhanced RetinaNet to detect masses in mammogram images. Specifically, we introduced a novel modification to the network structure, where the feature map M5 is processed by the ReLU function prior to the original convolution kernel. This strategic adjustment was designed to prevent the loss of resolution for small mass features. Additionally, we introduced transfer learning techniques into training process through leveraging pre-trained weights from other RetinaNet applications, and fine-tuned our improved model using the INbreast dataset.

Results

The aforementioned innovations facilitate superior performance of the enhanced RetiaNet model on the public dataset INbreast, as evidenced by a mAP (mean average precision) of 1.0000 and TPR (true positive rate) of 1.00 at 0.00 FPPI (false positive per image) on the INbreast dataset.

Conclusion

The experimental results demonstrate that our enhanced RetinaNet model defeats the existing models by having more generalization performance than other published studies, and it can also be applied to other types of patients to assist doctors in making a proper diagnosis.

CerviFusionNet: A multi-modal, hybrid CNN-transformer-GRU model for enhanced cervical lesion multi-classification

Cervical lesions pose a significant threat to women's health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor's experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification

Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature inconsistency and fluctuations in model parameter updates, which further contribute to decreased image classification accuracy and model instability. To address this issue, this paper proposes a novel method combining structural prior-driven feature extraction with gradient-momentum (SPGM), from the perspectives of consistent feature learning and precise parameter updates, to enhance the accuracy and stability of image classification. Specifically, SPGM leverages a structural prior-driven feature extraction (SPFE) approach to calculate gradients of multi-level features and original images to construct structural information, which is then transformed into prior knowledge to drive the network to learn features consistent with the original images. Additionally, an optimization strategy integrating gradients and momentum (GMO) is introduced, dynamically adjusting the direction and step size of parameter updates based on the angle and norm of the sum of gradients and momentum, enabling precise model parameter updates. Extensive experiments on CIFAR10 and CIFAR100 datasets demonstrate that the SPGM method significantly reduces the top-1 error rate in image classification, enhances the classification performance, and outperforms state-of-the-art methods.

Gene selection and cancer classification using interaction-based feature clustering and improved-binary Bat algorithm

In high-dimensional gene expression data, selecting an optimal subset of genes is crucial for achieving high classification accuracy and reliable diagnosis of diseases. This paper proposes a two-stage hybrid model for gene selection based on clustering and a swarm intelligence algorithm to identify the most informative genes with high accuracy. First, a clustering-based multivariate filter approach is performed to explore the interactions between the features and eliminate any redundant or irrelevant ones. Then, by controlling for the problem of premature convergence in the binary Bat algorithm, the optimal gene subset is determined using different classifiers with the Monte Carlo cross-validation data partitioning model. The effectiveness of our proposed framework is evaluated using eight gene expression datasets, by comparison with other recently published algorithms in the literature. Experiments confirm that in seven out of eight datasets, the proposed method can achieve superior results in terms of classification accuracy and gene subset size. In particular, it achieves a classification accuracy of 100% in Lymphoma and Ovarian datasets and above 97.4% in the rest with a minimum number of genes. The results demonstrate that our proposed algorithm has the potential to solve the feature selection problem in different applications with high-dimensional datasets.

Brain-computer interfaces inspired spiking neural network model for depression stage identification

BACKGROUND

Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Interfaces inspired deep learning-assisted diagnosis based on physiological signals holds promise for improving traditional methods lacking physiological basis and leads next generation neuro-technologies. However, traditional deep learning methods rely on immense computational power and mostly involve end-to-end network learning. These learning methods also lack physiological interpretability, limiting their clinical application in assisted diagnosis.

METHODOLOGY

A brain-like learning model for diagnosing depression using electroencephalogram (EEG) is proposed. The study collects EEG data using 128-channel electrodes, producing a 128×128 brain adjacency matrix. Given the assumption of undirected connectivity, the upper half of the 128×128 matrix is chosen in order to minimise the input parameter size, producing 8,128-dimensional data. After eliminating 28 components derived from irrelevant or reference electrodes, a 90×90 matrix is produced, which can be used as an input for a single-channel brain-computer interface image.

RESULT

At the functional level, a spiking neural network is constructed to classify individuals with depression and healthy individuals, achieving an accuracy exceeding 97.5 %.

COMPARISON WITH EXISTING METHODS

Compared to deep convolutional methods, the spiking method reduces energy consumption.

CONCLUSION

At the structural level, complex networks are utilized to establish spatial topology of brain connections and analyse their graph features, identifying potential abnormal brain functional connections in individuals with depression.

A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare

BACKGROUND

In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain.

NEW METHOD

A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions.

RESULTS

Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare.

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.