Abstract:
A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. The need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. This study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. Thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset.
