Deep Learning in Medical Image Analysis by R. Indrakumari (.PDF)

File Size: 10 MB

Deep Learning in Medical Image Analysis: Recent Advances and Future Trends by R. Indrakumari, T. Ganesh Kumar, D. Murugan, Sherimon P.C.
Requirements: .PDF reader, 10 MB
Overview: 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. 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.
Genre: Non-Fiction > Tech & Devices

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