Titlebook: Deep Learning for Biomedical Data Analysis; Techniques, Approach Mourad Elloumi Book 2021 Springer Nature Switzerland AG 2021 Deep Learning

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Medical Image Retrieval System Using Deep Learning Techniquesave discussed the different hand-crafted image features based retrieval systems to understand the perspectives of this research field. Here, we aim to congregate the weaknesses and constraints of the conventional retrieval systems and respective solutions with the help of the advanced DL algorithms.

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Deep Learning in Multi-Omics Data Integration in Cancer Diagnosticto be a different disease. That means the genomic activities varies among these different diseases and the normal tissue as well. Thanks to the power of computing, . (DL) techniques have become feasible to integrate multi-omics data generated from the cells/tissue to study the outcomes of cancer as

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Using Deep Learning with Canadian Primary Care Data for Disease Diagnosismmon diseases, the amount of available labeled data is often insufficient, and a variety of strategies are being explored to deal with inadequate, noisy and missing data. This chapter describes the benefits of using DL models with EMR data for research to improve provisioning of health care in prima

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Book 2021ications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data ana

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Book 2021, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader‘s head. It presents the results of the latest investig

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IoT Applications in Health Care,h dimensionality of microarray data and different deep learning classification techniques such as 2-. (2D- CNN) and 1-. CNN (1D-CNN). The proposed method used the fisher criterion and 1D-CNN techniques for microarray cancer samples prediction.