Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify.

• Chapter 1: Diagnosing and imaging in oral pathology by use of artificial intelligence and deep learning
• Chapter 2: Oral implantology with artificial intelligence and applications of image analysis by deep learning
• Chapter 3: Review of machine learning algorithms for breast and lung cancer detection
• Chapter 4: Deep learning for streamlining medical image processing
• Chapter 5: Comparative analysis of lumpy skin disease detection using deep learning models
• Chapter 6: Can AI-powered imaging be a replacement for radiologists?
• Chapter 7: Healthcare multimedia data analysis algorithms tools and techniques
• Chapter 8: Empirical mode fusion of MRI-PET images using deep convolutional neural networks
• Chapter 9: A convolutional neural network for scoring of sleep stages from raw single-channel EEG signals
• Chapter 10: Fundamentals, limitations, and the prospects of deep learning for biomedical image analysis
• Chapter 11: Impact of machine learning and deep learning in medical image analysis
• Chapter 12: Systemic review of deep learning techniques for high-dimensional medical image fusion
• Chapter 13: Qualitative perception of a deep learning model in connection with malaria disease classification
• Chapter 14: Analysis of preperimetric glaucoma using a deep learning classifier and CNN layer-automated perimetry
• Chapter 15: Deep learning applications in ophthalmology - computer-aided diagnosis
• Chapter 16: Brain tumor analyses adopting a deep learning classifier based on glioma, meningioma, and pituitary parameters
• Chapter 17: Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network
• Chapter 18: Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification
• Chapter 19: Deep learning applications in ophthalmology and computer-aided diagnostics
• Chapter 20: Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis