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Deep Learning Techniques for Biomedical Image Analysis in Healthcare

Deep Learning Techniques for Biomedical Image Analysis in Healthcare
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Author(s): Sivakami A. (Bharat Institute of Engineering and Technology, India), Balamurugan K. S. (Bharat Institute of Engineering and Technology, India), Bagyalakshmi Shanmugam (Sri Ramakrishna Institute of Technology, India) and Sudhagar Pitchaimuthu (Swansea University, UK)
Copyright: 2020
Pages: 16
Source title: Deep Neural Networks for Multimodal Imaging and Biomedical Applications
Source Author(s)/Editor(s): Annamalai Suresh (Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India), R. Udendhran (Department of Computer Science and Engineering, Bharathidasan University, India) and S. Vimal (Department of Information Technology, National Engineering College (Autonomous), Kovilpatti, India)
DOI: 10.4018/978-1-7998-3591-2.ch003

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Abstract

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.

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