Acral melanoma detection using dermoscopic images and convolutional neural networks.
Acral melanoma detection using dermoscopic images and convolutional neural networks.
复制标题
使用皮肤镜图像和卷积神经网络检测肢端黑色素瘤。
DOI：
10.1186/s42492-021-00091-z
复制
发表时间：
2021-10-07
影响因子：
--
通讯作者：
Ghani MU
中科院分区：
文献类型：
--
作者：
Abbas Q;Ramzan F;Ghani MU
肢端黑色素瘤(AM)是一种罕见且致命的皮肤癌。它可以由皮肤科专家使用皮肤镜成像进行诊断。由于黑色素瘤和非黑色素瘤癌症之间的微小差异，皮肤科医生诊断黑色素瘤是具有挑战性的。皮肤癌诊断的研究大多与黑色素瘤和非黑色素瘤病变的二进制分类有关。然而，到目前为止，对黑色素瘤亚型的分类研究还很有限。目前的研究调查了皮肤镜和深度学习在黑色素瘤亚型分类中的有效性，如AM在这项研究中，我们提出了一种新的深度学习模型，用于皮肤癌的分类。我们利用韩国延世大学健康系统的皮肤镜图像数据集对皮肤损伤进行分类。各种图像处理和数据增强技术已被应用于开发用于AM检测的健壮的自动化系统。我们的定制模型是一个从头开始训练的七层深度卷积网络。此外，迁移学习被用来比较我们的模型的性能，其中AlexNet和ResNet-18被修改、微调并在相同的数据集上进行训练。我们从我们提出的模型中获得了改进的结果，对AM和良性痣的准确率分别超过90 %。此外，使用转移学习方法，我们获得了接近97 %的平均准确率，这与最先进的方法相当。从我们的分析和结果中，我们发现我们的模型执行得很好，能够有效地对皮肤癌进行分类。结果表明，该系统可用于皮肤科医生对AM的早期诊断和临床决策
Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.
登录
查看更多内容
影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Thun, Michael J.
通讯作者：
Thun, Michael J.
影响因子：
3.9
作者：
Wang, Yongfeng;Yue, Wenwen;Yang, Guang
通讯作者：
Yang, Guang
影响因子：
3.9
作者：
Ali, Abder-Rahman H.;Li, Jingpeng;Yang, Guang
通讯作者：
Yang, Guang
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
2.6
作者：
Kassani, Sara Hosseinzadeh;Kassani, Peyman Hosseinzadeh
通讯作者：
Kassani, Peyman Hosseinzadeh