AI-assisted mole detection for online dermatology triage in telemedicine settings

Abstract

Skin moles are one of the most critical conditions for early diagnosis of severe conditions such as melanoma. Early identification of moles has become crucial nowadays, primarily due to malignant melanoma, a dangerous type of skin cancer. Most of the recent advances concerning this domain of dermatology deal mostly with either classifying moles as benign or malignant or with methods that help delineate moles from skin images. However, there are minimal sources of exploration in just determining whether moles are present in a given query image. In this paper, we have employed the latest state-of-the-art neural networks to identify the presence of moles in dermatological images uploaded by patients on an online teledermatology platform. This filter flags if a mole is detected, which can be employed as a triage system that helps the dermatologist diagnose and ease the follow-up treatment procedure in a physical setting if the patient has a mole in their image. A comparative study of the prediction performance of the different models has been provided for different performance metrics of interest. The results presented in this paper have been obtained from two sets of data, consisting of more than 26,000 clinical pictures with different conditions combined. Multiple experiments using different models yielded a macro average recall value as high as 0.955, along with overall accuracy and macro average precision values of 0.962 and 0.958, respectively.