Impact of Skin Lesion Descriptors and Deep Learning Architecture for the Effective Detection and Identification of Skin Diseases - A Systematic Review

Abstract

In diagnosing skin diseases, segmentation of lesion region and classification of detected lesion type are the two major processes. This paper conducts a systematic review related to the lesion descriptors extracted from the skin region and the machine learning classifiers utilized in differentiating the descriptor types and also discusses the deep learning schemes that impact skin lesion diagnosis. More specifically, the paper focuses on lesion region detection-based deep learning schemes that are derived from the Mask Region-based CNN (RCNN), U-Net, and DeepLabV3+ architectures.  In the case of skin lesion segmentation, the recent multi-attention scheme results in a recall, precision, and F1-score of 93.97%, 93.94%, and 93.73% respectively in the ISIC-2016 dataset which is higher than other lesion detection approaches. In the case of skin lesion classification, the Diverse CNN approach results in a maximum accuracy, recall, and precision of 96.12%, 93.11%, and 94.63% respectively when evaluated using the HAM-10000 dataset which is higher than other lesion classification approaches.