Skin Lession Classification Using Image Processing

Mr Sachin Mahadeo Bagade , Dr A M Patil , Prof O K Firke , Dr P M Mahajan

skin disease, gabor, knn

Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions result in an inaccurate diagnosis due to their high similarity. Accurate classification of the skin lesions in their early stages enables dermatologists to treat the patients and save their lives. This paper proposes a model for a highly accurate classification of skin lesions. The proposed model utilized the GLCM and Gabor features. Performance of SVM, KNN and Naïve Bayes Classifiers is evaluated. The latest well-known public challenge dataset, ISIC 2019, is used to test the ability of the proposed model to classify different kinds of skin lesions. The proposed model successfully classified the nine different classes of skin lesions, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and Squamous cell carcinoma. The achieved classification accuracy, using KNN and Gabor features are 96.07. The proposed model can detect images that do not belong to any one of the nine classes where these images are classified as unknown images.

Published Paper   E-Certificate

"Skin Lession Classification Using Image Processing", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.412 - 417, March-2023, Available :https://ijsdr.org/papers/IJSDR2303064.pdf

Volume 8 Issue 3, March-2023

Pages : 412 - 417

Paper Reg. ID: IJSDR_204420

Published Paper Id: IJSDR2303064

Downloads: 000347227

Research Area: Engineering

Country: Jalgaon, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2303064

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2303064

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition