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

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Shweta Mallick
S P Mishra


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

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Mallick, S. ., & Mishra, S. P. . (2023). Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 157–163.


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