This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.
The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
1. ANFIS BASED CARDIAC ARRHYTHMIA CLASSIFICATION 2. TWO-STAGE DEEP LEARNING ARCHITECTURE FOR CHEST X-RAY BASED COVID-19 PREDICTION 3. WHITE BLOOD CELLS CLASSIFICATION USING CONVENTIONAL AND DEEP LEARNING TECHNIQUES: A COMPARATIVE STUDY 4. COMPARISON AND PERFORMANCE EVALUATION USING CONVOLUTION NEURAL NETWORK BASED DEEP LEARNING MODELS FOR SKIN CANCER IMAGE CLASSIFICATION 5. A REVIEW ON BREAST CANCER DETECTION USING DEEP LEARNING TECHNIQUES 6. ARTIFICIAL INTELLIGENCE & MACHINE LEARNING: A SMART SCIENCE APPROACH FOR CANCER CONTROL 7. DETECTION OF DIABETIC FOOT ULCER USING MACHINE/ DEEP LEARNING 8. REVIEW ON DEEP LEARNING TECHNIQUES FOR PROGNOSIS AND MONITORING OF DIABETES MELLITUS