Skin cancer recognition using SVM image processing technique

Abstract

According to some estimates, skin cancer is the leading cause of death for people in the modern period. Although it can happen anywhere on the body, the majority of the time, this sort of cancer targets particular body regions that are more prone to being exposed to light. Skin cells proliferate in an erratic or patchy manner as a symptom. The majority of skin cancers can be treated if they are discovered early. Therefore, a patient’s life can be saved by promptly and precisely diagnosing skin cancer. Modern technology has made it feasible to detect skin cancer in its earliest stages. Skin cancer can be diagnosed systematically using the biopsy procedure (1). Skin cells are removed and supplied as a sample, following which they are evaluated in several laboratories. It’s an extremely unpleasant and time-consuming process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on support vector machine (SVM). It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called SVM are used in the identification process. In particular, epiluminescence microscopy is utilized using an image and several preprocessing techniques which are used in the reduction of sound artifacts and improve the quality of images. Segmentation is done by using certain thresholding techniques like Otsu. The gray level co-occurrence matrix technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The supervised learning model called SVM is used to distinguish data sets. It determines whether a picture is cancerous or not.