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Research Article |

Enhanced Diagnostic Methods for Identifying Anomalies in Imaging of Skin Lesions

Author(s): Ranjeet R. Suryawanshi1, Revanna C R2, B. Kameswara Rao3, Dankan Gowda V4 and Parismita Sarma5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 30 November 2022

e-ISSN : 2347-470X

Page(s) : 1077-1085

DOI: https://doi.org/10.37391/IJEER.100452

Abstract

There are several types of skin diseases, to protect and keep them healthy from these ailments; an effective and efficient diagnosis is required. One of the domains used by medical experts to diagnose severe class of skin disease is medical imaging. It is non-invasive way of diagnosis in which screen of the abnormal region performs first and then the dermatologist examines the subcutaneous structure and forecasts the severity of the lesion. One severe class of lesions is skin cancer, which is categorized as melanoma and non-melanoma. Most of the research has been performed on melanoma as yet and non-melanoma cancer diagnosis is still an untouched area. The cure rate of skin cancer is high, when diagnosed at an earlier stage. The proposed approach is applicable to gray scale or single channel images and the resultant output is binary images, and this can be compared easily with the available mask in the benchmark dataset. In addition to this, the APCNN proposal minimizes the requirement of post processing step for lesion boundary detection.

Keywords: Skin cancer, Boundary, Classification, Segmentation, Invasive and Medical Diagnosis.




Ranjeet Suryawanshi, Assistant Professor, Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth's college of Engineering, Kolhapur, Maharashtra, India; Email: rrs.bvcoek@gmail.com

Revanna C R, Associate Professor, Department of Electronics and Communication Engineering, Government Engineering College, Ramanagaram, Karnataka, India; Email: revannacr2008@gmail.com

B. Kameswara Rao*, Associate Professor, Department of computer science and Engineering, Gandhi Institute of Technology and Management, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India; Email: kamesh3410@gmail.com

Dankan Gowda V, Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bangalore, Karnataka, India; Email: dankan.v@bmsit.in

Parismita Sarma, Assistant Professor, Department of Information Technology, Gauhati University, Guwahati, Assam, India; Email: parismita.sarma@gmail.com

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Ranjeet Suryawanshi, Revanna C R, B. Kameswara Rao, Dankan Gowda V and Parismita Sarma (2022), Enhanced Diagnostic Methods for Identifying Anomalies in Imaging of Skin Lesions. IJEER 10(4), 1077-1085. DOI: 10.37391/IJEER.100452.