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Melanoma is one of the most serious skin cancers that can occur in any partof the human skin. Early diagnosis of melanoma lesions will significantlyincrease their chances of being cured. Improving melanoma segmentation willhelp doctors or surgical robots remove the lesion more accurately from bodyparts. Recently, the learning-based segmentation methods achieved desiredresults in image segmentation compared to traditional algorithms. This studyproposes a new approach to improve melanoma skin lesions detection andsegmentation by defining a two-step pipeline based on deep learning models. Ourmethods were evaluated on ISIC 2018 (Skin Lesion Analysis Towards MelanomaDetection Challenge Dataset) well-known dataset. The proposed methods consistof two main parts for real-time detection of lesion location and segmentation.In the detection section, the location of the skin lesion is precisely detectedby the fine-tuned You Only Look Once version 3 (F-YOLOv3) and then fed into thefine-tuned Segmentation Network (F-SegNet). Skin lesion localization helps toreduce the unnecessary calculation of whole images for segmentation. Theresults show that our proposed F-YOLOv3 performs better at 96% in mean AveragePrecision (mAP). Compared to state-of-the-art segmentation approaches, ourF-SegNet achieves higher performance with 95.16% accuracy.

The Fast and Accurate Approach to Detection and Segmentation of Melanoma  Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer  Learning