Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

Abstract

Purpose

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors.

Methods

In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM).

Results

The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features.

Conclusion

Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2igxU2J