This book introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book discusses multimedia data analysis algorithms and the principles of feature selection, optimisation and analysis.

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