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