This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses.
Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.
This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision.
Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions.
The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.
- 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
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.
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