Deep Learning and Computer Vision: Models and Biomedical Applications

Computer-Aided Diagnosis System for Liver Fibrosis Using Data Mining Techniques.- Deep learning for sequence alignment.- Protein Structure Prediction: A Computational Approach to Unravelling Molecular Mysteries.- Management of cancer-associated thrombosis and related complications.- Integrating Machine Vision for Enhanced Biomedical Signal and Image Processing.- Diagnostic strategies using AI and ML in cardiovascular diseases: Challenges and Future Perspectives.- Analytics of medical data using Cognos.- Analytics of Medical Data.- Integrating Deep Learning into Electronic Health Records: Opportunities and Challenges.- Heart disease prediction using machine learning algorithms and quantum variational classifier.- Deep Learning Based Approaches for Early Detection of Parkinson’s  Disease.- Exploring Recent Developments in Radiographic Chest Disease Detection through Deep Learning Models.- Nano encapsulation for the targeted drug delivery to enhance the efficacy of drugs.- Early Detection of Brain Tumor Automation System using Hybrid SMOTE ENN And Deep Convolution Neural Network Technique.- Advancements in Medical Device Integration Technology and its Impact on Healthcare.- Image Classification To Detect Breast Cancer Using Transfer Learning.- Medical Computer Vision.- Machine Vision & Biomedical Signal and Image Processing.- Metaheuristic Algorithms for Solving Various Optimization Problems: Comprehensive Review.- Maximizing Renewable Energy: Harnessing the Power of Metaheuristic Optimization Techniques.- Review on occurrence of skin lesions due to increased ultra-violet rays for diagnosis of skin cancer to sustain life using deep learning model.- Early Detection of  PCOD Automation System using Deep Convolution Neural Network Technique.- Design of Nano Magnetorheological fluid damper - based Leg prosthesis for Amputees.- Learning Analytics in Higher Education: Promises and Challenges.- Neural Network Fusion for Forgery Detection in Digital Images.