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

TL;DR

This paper introduces a two-step deep learning pipeline using fine-tuned YOLOv3 and SegNet for real-time melanoma detection and segmentation, achieving high accuracy on the ISIC 2018 dataset.

Contribution

The study presents a novel two-stage approach combining fine-tuned YOLOv3 and SegNet for improved melanoma detection and segmentation performance.

Findings

F-YOLOv3 achieves 96% mAP in lesion detection.

F-SegNet attains 95.16% accuracy in segmentation.

Method outperforms existing state-of-the-art approaches.

Abstract

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.