ARTIFICIAL INTELLIGENT SYSTEM FOR SKIN DISEASES CLASSIFICATION

Abstract

In this paper, an artificial intelligent technique is proposed for skin disease detection and classification. The suggested method comprises four stages, including segmentation, extraction of textural features, and classification. The stretch-based enhanced algorithm has been adapted for image enhancement. Then the method of an active contour is used for segmentation to determine the skin lesion in tissue. Textural features are obtained from the segmented skin lesion. As several numbers of the features can affect the classification precision, ideal feature selection is made to exclude features that are less informative and unnecessary. The feature selection is adjusted with a regularized random forest. Finally, the classification algorithms by support vector machine and a back-propagation neural network (BPNN) are implemented. The dataset consists of 400 dermoscopic images in total divided into 200 benign and 200 malignant skin diseases extracted from the dermoscopic images PH2 database. The result of detecting and classifying the dermoscopic images on these images yielded an accuracy of 99.7%, a sensitivity of 99.4%, and a specificity of 100% by BPNN. The experiential results confirmed that the BPNN classifier is best rather than an SVM classifier for skin disease images. This proposed model will be advanced to support the skin image processing techniques that provided a more accurate diagnosis and rapid treatment plan.