Skin Cancer Detection Using Machine Learning

Abstract

Now a day’s skin cancer is a major problem human beings are facing, To recognize skin cancer new methodology for diagnosing skin cancer by images of dermatologic spots using image processing is presented. Currently, skin cancer is one of the most frequent diseases in humans. This methodology is based on Fourier spectral analysis using filters such as classic, inverse, and to k-law nonlinear. The sample images are obtained by a specialist as a replacement spectral technique is developed and quantitative measurement in the complex pattern found cancerous skin spots. Finally which spectral index is calculated to get a variety of spectral indices defined for carcinoma. Our results show a confidence of level 95.4%. carcinoma mainly occurs thanks to exposure to sunlight. Skin Cancer alarming is disease for mankind, the need for early diagnosis the skin cancer are increased due to the rapid climb rate of Melanoma skin cancer, its high treatment Costs, and the death rate. The cancer cells are detected manually and it takes time to cure in most of the cases. This project proposed a man-made carcinoma detection system using image Processing and machine learning method. The features of the affected skin cells are extracted after the segmentation of the pictures using the feature extraction technique. A deep learning-based method Convolutional neural network classifier is employed for the stratification of the extracted features.