Department of Biomedical Engineering, Keimyung University, Daegu 42601, Korea.
Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA.
Sensors (Basel). 2021 Aug 18;21(16):5551. doi: 10.3390/s21165551.
Malignant melanoma accounts for about 1-3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.
恶性黑素瘤约占西方所有恶性肿瘤的 1-3%，尤其是在美国。每年有超过 9000 人死亡。一般来说，很难从照片上识别皮肤病变。在本文中，我们提出了一种基于深度学习的计算机辅助诊断算法，用于从 RGB 通道皮肤图像中对恶性黑素瘤和良性皮肤肿瘤进行分类。所提出的深度学习模型构成了肿瘤病变分割模型和恶性黑素瘤分类模型。首先，使用 U-Net 对皮肤镜图像中的皮肤病变进行分类。我们使用皮肤病变图像和卷积神经网络的专家标记结果实现了一种分类恶性黑素瘤和良性肿瘤的算法。与专家标记结果相比，U-Net 模型的骰子相似系数达到 81.1%。恶性黑素瘤的分类准确率达到 80.06%。因此，预计所提出的人工智能算法将被用作计算机辅助诊断算法，以帮助早期发现恶性黑素瘤。
