Skin Cancer Detection using Machine Learning Techniques

As increasing instant of skin cancer every year with regards of malignant melanoma, the dangerous type of skin cancer. And the detection of skin cancer is difficult from the skin lesion due to artifacts, low contrast, and similar visualization like mole, scar etc. Hence Automatic detection of skin lesion is performed using techniques for lesion detection for accuracy, efficiency and performance criteria. The proposed algorithm applies feature extraction using ABCD rule, GLCM and HOG feature extraction for early detection of skin lesion. In the proposed work, Pre-processing is to improve the skin lesion quality and clarity to reduce artifacts, skin color, hair, etc., Segmentation was performed using Geodesic Active Contour (GAC) which segments the lesion part separately which was further useful for feature extraction. ABCD scoring method was used for extracting features of symmetry, border, color and diameter. HOG and GLCM was used for extracting textural features. The extracted features are directly passed to classifiers to classify skin lesion between benign and melanoma using different machine learning techniques such as SVM, KNN and Naive Bayes classifier. In this project skin lesion images were downloaded from International Skin Imaging Collaboration (ISIC) in which 328 images of benign and 672 images of melanoma. The classification result obtained is 97.8 % of Accuracy and 0.94 Area under Curve using SVM classifiers. And additionally the Sensitivity obtained was 86.2 % and Specificity obtained was 85 % using KNN.