Machine Learning in Medical Imaging and Computer Vision

Amita Nandal is an Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. She has authored or co-authored over 40 scientific articles in the area of image processing, wireless communication, and parallel architectures. Her research interests include image processing, machine learning, deep learning, and digital signal processing. Liang Zhou is an Associate Professor at the Shanghai University of Medicine & Health Sciences, China. He received his PhD degree from the Donghua University, Shanghai, China, in 2012. His research interests are focused in the areas of big data analysis and machine learning with applications in the field of medicine and healthcare. Arvind Dhaka is an Associate Professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has authored or co-authored over 40 scientific articles in the area of image processing, wireless communication, and network security. His research interests include image processing, machine learning, wireless communication, and wireless sensor networks. Todor Ganchev is a Professor in the Department of Computer Science and Engineering and the Head of the Artificial Intelligence Laboratory at the Technical University of Varna, Bulgaria. He has authored/co-authored over 180 publications in topics, including biometrics, physiological signal processing, machine learning and its applications. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). Farid Nait-Abdesselam is a Professor in the School of Science and Engineering at the University of Missouri Kansas City, USA. His research interests include security and privacy, networking, internet of things, and healthcare systems. He has authored/co-authored over 150 research papers in these areas.

Chapter 1: Machine learning algorithms and applications in medical imaging processing
Chapter 2: Review of deep learning methods for medical segmentation tasks in brain tumors
Chapter 3: Optimization algorithms and regularization techniques using deep learning
Chapter 4: Computer-aided diagnosis in maritime healthcare: review of spinal hernia
Chapter 5: Diabetic retinopathy detection using AI
Chapter 6: A survey image classification using convolutional neural network in deep learning
Chapter 7: Text recognition using CRNN models based on temporal classification and interpolation methods
Chapter 8: Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection
Chapter 9: Medical image classification and retrieval using deep learning
Chapter 10: Game theory, optimization algorithms and regularization techniques using deep learning in medical imaging
Chapter 11: Data preparation for artificial intelligence in federated learning: the influence of artifacts on the composition of the mammography database
Chapter 12: Spatial cognition by the visually impaired: image processing with SIFT/BRISK-like detector and two-keypoint descriptor on Android CameraX
Chapter 13: Feature extraction process through hypergraph learning with the concept of rough set classification
Chapter 14: Machine learning for neurodegenerative disease diagnosis: a focus on amyotrophic lateral sclerosis (ALS)
Chapter 15: Using deep/machine learning to identify patterns and detecting misinformation for pandemics in the post-COVID-19 era
Chapter 16: Integrating medical imaging using analytic modules and applications