Deep Learning in Medical Imagine and Drug Design

Surayya Ado Bala (Department of Computer Science and Engineering, Sharda University , India)
Shri Ojha Kant (Research and Technology Development Center, Sharda University, India)
Adamu Garba Yakasai (School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China)

Article ID: 2683


Over the last decade, deep learning (DL) methods have been extremely successful and widely used in almost every domain. Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy. DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data. Medical imaging has transformed healthcare science, it was thought of as a diagnostic tool for disease, but now it is also used in drug design. Advances in medical imaging technology have enabled scientists to detect events at the cellular level. The role of medical imaging in drug design includes identification of likely responders, detection, diagnosis, evaluation, therapy monitoring, and follow-up. A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making. For this, a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment. The result is a quantifiable improvement in healthcare quality in most therapeutic areas, resulting in improvements in quality and life duration. This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design. We briefly discuss the fields related to the history of deep learning, medical imaging, and drug design.


Deep Learning;Medical Imaging;Drugs Design;Cheminformatics

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