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.

• 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