MELANOMA SKIN CANCER DETECTION USING DEEP LEARNING AND CLASSICAL MACHINE LEARNING TECHNIQUES: A HYBRID APPROACH

A new method for diagnosing skin cancer based on images of dermatologic spots using image processing was presented because skin cancer is a major issue that people face today. Skin cancer is currently one of the most prevalent human diseases. This method employs classical, inverse, and k-law nonlinear filters in Fourier spectral analysis. A specialist obtains the sample images as a replacement spectrum for the developed technique and quantitative measurement of the intricate pattern of cancerous skin spots. Last but not least, which spectral index is used to determine the variety of carcinomas Our findings indicate a level of confidence of 95.4 percent that exposure to sunlight is primarily the cause of carcinoma. Ozone depletion and ongoing chemical exposure are two of the other factors that contribute to the development of carcinoma. Carcinogenesis involves UV-induced mutations of the p53 gene. The P53 gene is important for SCC development. Because of the rapidly increasing incidence of melanoma skin cancer, its high treatment costs, and the high death rate, early detection of skin cancer is becoming increasingly important. The majority of cases require prolonged treatment because the cancer cells are manually detected. A human- origin carcinoma detection system based on image processing and machine learning was proposed in this project. After the pictures have been segmented, the feature extraction technique is used to extract the features of the affected skin cells. The extracted features are stratified using a Convolutional neural network classifier that is based on deep learning. Skin cancer is a serious problem that needs to be caught early. The diagnostic is a time-consuming and expensive manual procedure. However, the use of machine learning, in particular a convolution neural network, has made it easier than ever to identify cancerous cells in today’s scientific world. This makes it possible to identify cancerous cells more quickly and effectively.