IMR Press / FBL / Volume 24 / Issue 3 / DOI: 10.2741/4725

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
State-of-the-art review on deep learning in medical imaging
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1 Department of Computer Science and Engineering, National Institute of Technology Goa, India
2 Department of Radiology, A.O.U., Italy
3 Brown University, Providence, RI, USA
4 IMIM, Hospital del Mar, Barcelona, Spain
5 Dept. of Cardiology, St. Helena Hospitals, St. Helena, CA, USA
6 Liver Unit, Department of Gastroenterology and Hepatology, Hospital de Santa Maria, Medical School of Lisbon, Lisbon 1629-049, Portugal
7 ISR, Instituto Superior Tecnico (IST), Lisboa
8 Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
9 Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, California, USA
10 Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA
Send correspondence to: Jasjit S. Suri, Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA and Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, California, Tel: 916-797-5942, Fax: 916-797-5942, E-mail:
Front. Biosci. (Landmark Ed) 2019, 24(3), 380–406;
Published: 1 January 2019

Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought process takes place. Therefore, like the brain, it tries to learn and recognize patterns in the form of digital images. This power is built on the depth of many layers of computing neurons backed by high power processors and graphics processing units (GPUs) easily available today. In the current scenario, we have provided detailed survey of various types of DL systems available today, and specifically, we have concentrated our efforts on current applications of DL in medical imaging. We have also focused our efforts on explaining the readers the rapid transition of technology from machine learning to DL and have tried our best in reasoning this paradigm shift. Further, a detailed analysis of complexities involved in this shift and possible benefits accrued by the users and developers.

Deep Learning
Medical Imaging
Figure 1
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