A survey on automated medical image classification using deep learning

Swaroop Hebbale; Ashapurna Marndi; B H Manjunatha Kumar; B R  Mohan; Prasad N Achyutha; Piyush Kumar Pareek

doi:10.53730/ijhs.v6nS1.6791

Authors

Swaroop Hebbale
swaroop.150051@gmail.com
Post Graduation Student, Master of Technology, Department of Computer Science and Engineering, East West Institute of Technology, Bengaluru, Karnataka, India; Pincode:- 560091
Ashapurna Marndi Senior Scientist, CSIR Fourth Paradigm Institute, NAL Belur Campus, Bangalore, Karnataka, India; Pincode:- 560037
Manjunatha Kumar B H Professor, Department of Computer Science and Engineering, SJC Institute of Technology, Chickballapur, Karnataka, India; Pincode:- 562101
B R Mohan Professor, Department of Computer Science and Engineering, East West Institute of Technology, Bengaluru, Karnataka, India; Pincode:- 560091
Achyutha Prasad N Professor, Department of Computer Science and Engineering, East West Institute of Technology, Bengaluru, Karnataka, India; Pincode:- 560091
Piyush Kumar Pareek Professor, Department of Computer Science and Engineering & Head of IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India; Pincode:- 560064

Keywords:

medicinal image classification, deep learning, supervised learning, poorly supervised learning

Abstract

Deep Learning has indeed been widely used in many fields/areas of medicinal images classification, and a large number of publications have been published documenting its success. The key for achieving effective diagnosis and therapy is accurate characterization of medical pictures. However, because image interpretation is highly dependent on the subjective opinion of doctors, the results of image processing vary greatly amongst clinicians at different levels. Picture classification, target identification, and image analysis have all improved dramatically in recent years with environmental image data sets in domain of Image Processing. In this paper, we have presented a systematic survey on feature extracting and classifying the medical images using deep learning methods. Two new ideas are presented in this work. First and foremost, we classified presently trendy publications in a multi-level configuration. Second, this research article concentrates on supervised and poorly supervised learning techniques.

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