Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples.
Features:
Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis
Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges
Focuses on aspects of deep learning and machine learning for combating COVID-19
Includes pertinent case studies
This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
1 Medical Image Detection and Recognition Using Machine Learning and Deep Learning 2 Multiple Lung Disease Prediction Using X-Ray Images Based on Deep Convolutional Neural Networks 3 Analysis of Machine Learning and Deep Learning in Health Informatics, and Their Application 4 Automated Acute Lymphoblastic Leukemia Detection Using Blood Smear Image Analysis 5 Smart Digital Healthcare Solutions Using Medical Imaging and Advanced AI Techniques 6 Efficient and Fast Lung Disease Predictor Model 7 Artificial Intelligence Used to Recognize Fetal Planes Based on Ultrasound Scans during Pregnancy 8 Artificial Intelligence Techniques for Cancer Detection from Medical Images 9 Handling Segmentation and Classification Problems in Deep Learning for Identification of Interstitial Lung Disease 10 Computer Vision Approaches in Radiograph Image Analysis: A Targeted Review of Current Progress, Challenges, and Future Perspective 11 Deep Learning Methods for Brain Tumor Segmentation 12 Face Mask Detection and Temperature Scanning for the COVID-19 Surveillance System Based on Deep Learning Models 13 Diabetic Disease Prediction Using Machine Learning Models and Algorithms for Early Classification and Diagnosis Assessment 14 Defeating Alzheimer's: AI Perspective from Diagnostics to Prognostics: Literature Summary
Ben Othman Soufiene was Assistant Professor of computer science at the University of Gabes, Tunisia, from 2016 to 2021. He received his PhD in computer science from Manouba University in 2016 for his dissertation on secure data aggregation in wireless sensor networks. He also earned an MS from Monastir University in 2012. His research interests focus on the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning, and big data. Chinmay Chakraborty is Assistant Professor in the Department of Electronics and Communication Engineering, BIT Mesra, India, and a Postdoctoral Fellow of the Federal University of Piauí, Brazil. His primary areas of research include wireless body area networks, Internet of Medical Things (IoMT), point-of-care diagnosis, mHealth/e-health, and medical imaging. Chakraborty is the co-editor of many books on Smart IoMT, healthcare technology, and sensor data analytics.
