Publisher Description

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.Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties.Deep Learning in Medical Image Processing and Analysis introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed.Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models.

Table of Contents

• 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

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