论文标题
基于深度转移学习的微调Yolov3和Segnet，快速准确地检测和分割黑色素瘤皮肤癌
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning
论文作者
论文摘要
黑色素瘤是可能发生在人类皮肤任何部分的最严重的皮肤癌之一。黑色素瘤病变的早期诊断将显着增加康复的机会。改善黑色素瘤细分将有助于医生或手术机器人从身体部位更准确地清除病变。最近，与传统算法相比，基于学习的分割方法在图像分割中实现了预期的结果。这项研究提出了一种新的方法，通过定义基于深度学习模型的两步管道来改善黑色素瘤皮肤病变检测和分割。对ISIC 2018（针对黑色素瘤检测挑战数据集的皮肤病变分析）评估了我们的方法。提出的方法由两个主要部分组成，用于实时检测病变位置和分割。在检测部分中，精细调整的皮肤病变的位置只能在版本3（F-Yolov3）中查看，然后送入微调分割网络（F-segnet）。皮肤病变定位有助于减少整个图像的不必要计算以进行分割。结果表明，我们提出的F-YOLOV3在平均平均精度（地图）上以96％的效果表现更好。与最先进的细分方法相比，我们的F-segnet以95.16％的精度达到了更高的性能。
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.
