  Skin cancer, a major form of cancer, is a critical public health problem with
123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma
cases worldwide each year. The leading cause of skin cancer is high exposure of
skin cells to UV radiation, which can damage the DNA inside skin cells leading
to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed
visually employing clinical screening, a biopsy, dermoscopic analysis, and
histopathological examination. It has been demonstrated that the dermoscopic
analysis in the hands of inexperienced dermatologists may cause a reduction in
diagnostic accuracy. Early detection and screening of skin cancer have the
potential to reduce mortality and morbidity. Previous studies have shown Deep
Learning ability to perform better than human experts in several visual
recognition tasks. In this paper, we propose an efficient seven-way automated
multi-class skin cancer classification system having performance comparable
with expert dermatologists. We used a pretrained MobileNet model to train over
HAM10000 dataset using transfer learning. The model classifies skin lesion
image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36
percent and top3 accuracy of 95.34 percent. The weighted average of precision,
recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The
model has been deployed as a web application for public use at
(https://saketchaturvedi.github.io). This fast, expansible method holds the
potential for substantial clinical impact, including broadening the scope of
primary care practice and augmenting clinical decision-making for dermatology
specialists.
