Deep Learning in Medical Image Analysis: Recent Advances and Future TrendsКНИГИ » ПРОГРАММИНГ
Название: Deep Learning in Medical Image Analysis: Recent Advances and Future Trends Автор: R. Indrakumari, T. Ganesh Kumar, D. Murugan, Sherimon P.C. Издательство: CRC Press Серия: Artificial Intelligence in Smart Healthcare Systems Год: 2025 Страниц: 197 Язык: английский Формат: pdf (true) Размер: 10.1 MB
The proposed book is designed as a reference text and provides a comprehensive overview of conceptual and practical knowledge about Deep Learning in medical image processing techniques. The post-pandemic situation teaches us the importance of doctors, medical analysis, and diagnosis of diseases in a rapid manner. This book provides a snapshot of the state of current research between Deep Learning, medical image processing, and healthcare with special emphasis on saving human life. The chapters cover a range of advanced technologies related to patient health monitoring, predicting diseases from genomic data, detecting artefactual events in vital signs monitoring data, and managing chronic diseases.
Neural networks are Machine Learning algorithms that are modelled after the functionality and organization of the human brain. Neurons, which are interconnected nodes that communicate with one another to process and analyze input, make up these systems. A neural network’s neurons are arranged into layers, each layer handling a particular component of data processing. While the output layer creates the network’s ultimate output, the input layer receives the raw data. In between, there can be one or more hidden layers that perform complex computations on the data.
Deep Learning, a subtype of Machine Learning, uses deep neural networks, which are neural networks with several layers. Deep Learning algorithms can learn from vast amounts of data and can automatically extract complex features and patterns from the input data. The layers of interconnected neurons that make up deep neural networks each process the output from the layer before it. Depending on how difficult the problem being handled is, a deep neural network may have a few, hundreds, or even thousands of layers. Deep Learning has had great success in many different areas, including natural language processing, recognizing images, and recognizing speech. Deep neural networks, for instance, have been applied to create extremely precise image identification systems, such as those used in self-driving cars and facial recognition technology.
•Delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. •Presents key principles by implementing algorithms from scratch and using simple MATLAB/Octave scripts with image data. •Provides an overview of the physics of medical image processing alongside discussing image formats and data storage, intensity transforms, filtering of images and applications of the fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction •Highlights the new potential applications of machine learning techniques to the solution of important problems in biomedical image applications.
The book is for students, scholars and professionals of biomedical technology and healthcare data analytics.
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Deep Learning in Medical Image Analysis: Recent Advances and Future Trends 
