Skin Cancer Detection And Classification Using Svm Classifier (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Human cancer is the most hazardous sicknesses existing which is principally brought about by hereditary flimsiness of numerous atomic changes. Among the numerous kinds of disease, skin cancer is quite possibly the most widely recognized sorts of malignancy. There are three kinds of skin malignant growth, to be specific, Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC)and Melanoma, melanoma is the sort of skin cancer which is perilous. The skin cancer detection technology is extensively isolated into four fundamental parts beginning from gathering dermoscopic image data set, dermoscopic image database, image pre-processing which includes hair removal, noise removal, sharpening, resize, contrast stretching of the given skin image, segmentation in which gave for segmenting the zone of interest from the given image. Various methods can be utilized for segmentation. Some regularly utilized division calculations are k-means, thresholding histogram and so on, feature extraction from the portioned picture and grouping of the picture from the feature set separated from sectioned picture. Various classification algorithms are used for this, among which the utilization of machine learning and deep learning-based algorithm are used to improve results for classification. The most frequently utilized classification. algorithms are ‘support vector machine’, ‘feedforward artificial neural network’, ‘deep convolutional neural network’. This paper provides the two types of skin cancer - Basal Cell Carcinoma  and Melanoma and equally threatening (skin) diseases such as Actinic keratosis, Cherry nevus, Dermatofibroma and Melanocytic nevus, and classify them into six different classes using the ‘support vector machine (SVM) classifier’.