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Full Description
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.
Contents
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
