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

• URL: http://arxiv.org/abs/2210.05167v1
• Date: Tue, 11 Oct 2022 06:09:44 GMT
• Title: The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning
• Authors: Mohamad Taghizadeh, Karim Mohammadi
• Abstract summary: Melanoma is one of the most serious skin cancers that can occur in any part of the human skin. Recently, the learning-based segmentation methods achieved desired results in image segmentation compared to traditional algorithms. This study proposes a new method to improve melanoma skin lesions detection and segmentation by defining a two-step pipeline based on deep learning models.
• Score: 0.6091702876917281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Melanoma is one of the most serious skin cancers that can occur in any part of the human skin. Early diagnosing 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 method 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 achieves better performance as 96% in mAP. Compared to state-of-the-art segmentation approaches, our F-SegNet achieves higher performance for accuracy, dice coefficient, and Jaccard index at 95.16%, 92.81%, and 86.2%, respectively.

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