Deep Learning in Skin Lesion Analysis Towards Cancer Detection

Detecting Melanoma, the deadliest skin cancer, at the early stage can exceptionally escalate the possibility of the cure up to 99.2% 5-year survival rate. Manual examination by the dermatologist continues to be used as the core and most trusted method till today, albeit a decade of effort in using the technology. Therefore, given the low supply of dermatologists, it is impossible to proactively run the surveillance on people at highest risk for early finding. Deep Convolutional Neural Networks (DCNNs) have demonstrated a dramatic breakthrough in automatic skin lesion classification, which is imperative to improve the diagnostic performance over the mass population with limited access to specialists. Web-application based dermoscopic imaging with integrated artificial intelligence (AI) is a plausible accessible method for future skin lesion analysis. The artificially intelligent plugin, consequently, can serve as enablers for the dermatology community. In this paper, we report the findings of our investigation of using DCNNs for automated Melanoma region segmentation in dermoscopy images. For training and evaluation, we use the HAM10000 public dataset. We aim to realize our model by creating a web tool that can tell general practitioners (GP) and lab technologists the probability diagnoses for a given skin lesion. This automation will assist in fast segregation of the high-risk patients and speed up the follow-up diagnosis and treatment workflow.