Computer aided diagnosis system for skin lesions detection using texture analysis methods

Malignant melanoma is considered as one of the most dangerous type of skin cancers as it increases the mortality rate. Computer-aided diagnosis (CAD) systems can help to detect melanoma early. In this paper, we propose a new skin melanoma CAD system using texture analysis methods. The proposed CAD system consists of four steps: hair removal, filtering, feature extraction and classification. In the feature extraction step, we evaluate the performance of five widely used texture analysis methods: grey level co-occurrence matrix, Gabor filters, a histogram of oriented gradients, local binary pattern and local directional number. Our CAD system classifies between non-melanoma skin lesions (represented as common nevi or dysplastic nevi) and melanoma. The experimental results show that extracting HOG features after hair removal yields the best classification results. HOG gives an AUC of 0.9783 with melanoma/common nevi classification and an AUC of 0.9439 with melanoma/dysplastic nevi classification.