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This book 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 supported by case studies.
List of contents
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
About the author
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.
