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Technology-assisted platforms provide reliable solutions in almost everyfield these days. One such important application in the medical field is theskin cancer classification in preliminary stages that need sensitive andprecise data analysis. For the proposed study the Kaggle skin cancer dataset isutilized. The proposed study consists of two main phases. In the first phase,the images are preprocessed to remove the clutters thus producing a refinedversion of training images. To achieve that, a sharpening filter is appliedfollowed by a hair removal algorithm. Different image quality measurementmetrics including Peak Signal to Noise (PSNR), Mean Square Error (MSE), MaximumAbsolute Squared Deviation (MXERR) and Energy Ratio/ Ratio of Squared Norms(L2RAT) are used to compare the overall image quality before and after applyingpreprocessing operations. The results from the aforementioned image qualitymetrics prove that image quality is not compromised however it is upgraded byapplying the preprocessing operations. The second phase of the proposedresearch work incorporates deep learning methodologies that play an imperativerole in accurate, precise and robust classification of the lesion mole. Thishas been reflected by using two state of the art deep learning models:Inception-v3 and MobileNet. The experimental results demonstrate notableimprovement in train and validation accuracy by using the refined version ofimages of both the networks, however, the Inception-v3 network was able toachieve better validation accuracy thus it was finally selected to evaluate iton test data. The final test accuracy using state of art Inception-v3 networkwas 86%.

Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages