  Skin cancer is considered to be the most common human malignancy. Around 5
million new cases of skin cancer are recorded in the United States annually.
Early identification and evaluation of skin lesions is of great clinical
significance, but the disproportionate dermatologist-patient ratio poses
significant problem in most developing nations. Therefore a deep learning based
architecture, known as SkiNet, is proposed with an objective to provide faster
screening solution and assistance to newly trained physicians in the clinical
diagnosis process. The main motive behind Skinet's design and development is to
provide a white box solution, addressing a critical problem of trust and
interpretability which is crucial for the wider adoption of Computer-aided
diagnosis systems by the medical practitioners. SkiNet is a two-stage pipeline
wherein the lesion segmentation is followed by the lesion classification. In
our SkiNet methodology, Monte Carlo dropout and test time augmentation
techniques have been employed to estimate epistemic and aleatoric uncertainty,
while saliency-based methods are explored to provide post-hoc explanations of
the deep learning models. The publicly available dataset, ISIC-2018, is used to
perform experimentation and ablation studies. The results establish the
robustness of the model on the traditional benchmarks while addressing the
black-box nature of such models to alleviate the skepticism of medical
practitioners by incorporating transparency and confidence to the model's
prediction.
