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