An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017

TL;DR

This paper presents an automated melanoma detection method using image segmentation, feature extraction, and classification, aiming to improve diagnosis accuracy and treatment planning.

Contribution

It introduces a novel multi-phase approach combining CIELAB segmentation and O-A SVM classification for melanoma detection.

Findings

Effective segmentation using CIELAB color space

High classification accuracy on ISIC 2017 dataset

Potential for improved diagnostic support

Abstract

An automated method to detect and analyze the melanoma is presented to improve diagnosis which will leads to the exact treatment. Image processing techniques such as segmentation, feature descriptors and classification models are involved in this method. In the First phase the lesion region is segmented using CIELAB Color space Based Segmentation. Then feature descriptors such as shape, color and texture are extracted. Finally, in the third phase lesion region is classified as melanoma, seborrheic keratosis or nevus using multi class O-A SVM model. Experiment with ISIC 2017 Archive skin image database has been done and analyzed the results.