Skin Cancer prediction using Machine Learning

Despite being one of the worst kinds of cancer, skin cancer deaths have risen rapidly in recent years. Lack of education about the disease's warning signals and the Identifying cancer early, when it's still treatable, is crucial to preventing its spread. Melanoma, basal cell carcinoma, and squamous cell carcinoma are deadly skin cancers. Atypical basal cell carcinoma and squamous cell carcinoma are other skin cancers. This study uses machine learning and image processing to classify skin cancers. Before preprocessing, dermoscopy pictures are entered. After removing unwanted hair with a dull razor, a Gaussian filter is used to smooth the image. The median filter filters noise and maintains lesion margins. In the segmentation step, color-based k-means clustering is used because colour is an important factor in determining malignancy. ABCD and Gray Level Cooccurrence Matrix extract statistical and textural characteristics. Asymmetry, border colour, and diameter (GLCM). The ISIC 2019 Challenge dataset contains eight kinds of dermoscopic pictures. For classification, a Multi-class Support Vector Machine (MSVM) was built with 96.25 percent accuracy.