Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter

Many techniques related to image analysis have been proposed by researchers which are being used detect a large number of diseases. These images carefully analyzed radiologists and doctors, after careful interpretation, the results obtained finally help in making an appropriate diagnosis. This is complicated time consuming task, requires high levels concentration. Therefore, experts who analyze mustn't suffer from fatigue or other common problems that can impair their performance. The present study attempts reveal how deep learning model using CNN with VGG16 effective for diagnosis detection skin cancer at its early stages. Therefore under scheme, 4000 raw tissues evaluated. diagnostic starts pre-processing CLAHE along inculcation Gaussian filter. Thereafter, hyper-parameter optimizer stochastic gradient descent, rate 0.001, incorporating training epochs 50 nos. pertaining batch size 32 formed. Consequently, as result, accuracy achieved 99.70%, loss value 0.0055%, precision 99.75%, recall f1-score 99.50% respectively.