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.
翻译:皮肤瘤是人类皮肤任何部分中可能发生的最严重的皮肤癌症之一。 早期诊断乳腺瘤损伤将大大增加治愈的机会。 改善乳腺瘤分解将有助于医生或手术机器人更准确地从身体部位中消除损伤。 最近, 学习为基础的分解方法在图像分解方面与传统算法相比取得了预期结果。 本研究提出一种新的方法, 通过根据深层次学习模式界定双步分解管道, 改善对乳腺皮肤损伤的检测和分解。 我们的方法在2018年的ISIC( 皮肤乳腺分析, 了解挑战数据集) 上广为人知的数据集中进行了评估。 提议的方法包括两种主要部分, 用于实时检测损伤位置和分解部分。 在检测部分中, 皮肤损伤的位置由精密的《你一看一看第三版》(F-YOLOv3) 精确检测出来, 然后输入微调分解的分解网络(F- SegNet ) 。 Skin 分解有助于减少不必要地计算整个图像的精确度。 在F- AL- S- S-AD- Preal 显示我们在 FAD- Preal- Prealation 96- FAD- FAD- FAD- SAL- SAL- VAL- SAL- SAL- SAL- SALD- FAD- FAD- FAD- FAD- SAL- SAL- SAL- FAL- SAL- SAL- SAL- SAL- SAL- SAL-S- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SALD-S-S-S- SAL- SALD-S- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL- SAL-S- SAL- SAL- SAL-S-S-S-S- SAL- FAL- SAL- SAL-S- SAL- SAL-S-S-S-S-S-S-S- S-S-S-S-S- SAL- FAL- S- S- S- S- S- S- S- S- S-