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

Maram A Wahba¹, Amira S Ashour¹, Sameh A Napoleon¹

¹Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.

Health Information Science and Systems

|November 17, 2017

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.

Keywords:

Basal cell carcinoma Empirical mode decomposition Gray-level difference method Riesz Skin cancer classification Support vector machine

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Keywords:

Basal cell carcinoma Empirical mode decomposition Gray-level difference method Riesz Skin cancer classification Support vector machine

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