Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis

Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.

Lung cancer histopathological image classification using wavelets and AlexNet

Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.

Deep Learning Techniques to Diagnose Lung Cancer

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system

The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN)

Purpose

A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies.

Methods

The NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model.

Results

The proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies.

Conclusion

The proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.