Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

中文翻译：

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使用深度学习的色素沉着皮肤病变皮肤癌分类系统的开发。

最近的研究表明，卷积神经网络（CNN）可以对黑素瘤图像进行分类，其准确性与皮肤科医生所能达到的相当。然而，迄今为止，尚未报告与皮肤科医生竞争的在临床图像分类任务中仅用色素性皮肤病变的临床图像训练的CNN的性能。在这项研究中，我们从3551例患者中提取了5846例皮肤色素沉着病变的临床图像。色素沉着的皮肤病变包括恶性肿瘤（恶性黑色素瘤和基底细胞癌）和良性肿瘤（痣，脂溢性角化病，老年性扁桃体以及血肿/血管瘤）。我们通过随机选择666位患者并为每位患者选择一张图像来创建测试数据集，并通过为其余图像（4732张图像，2885位患者）提供边界框注释来创建训练数据集。随后，我们使用训练数据集训练了一个更快的基于区域的CNN（FRCNN），并在测试数据集上检查了模型的性能。此外，十名获得董事会认证的皮肤科医生（BCD）和十名皮肤科实习生（TRN）接受了相同的测试，我们将它们的诊断准确性与FRCNN进行了比较。对于六类分类，FRCNN的准确性为86.2％，而BCD和TRN的准确性为79.5％（我们将它们的诊断准确性与FRCNN进行了比较。对于六类分类，FRCNN的准确性为86.2％，而BCD和TRN的准确性为79.5％（我们将它们的诊断准确性与FRCNN进行了比较。对于六类分类，FRCNN的准确性为86.2％，而BCD和TRN的准确性为79.5％（p = 0.0081）和75.1％（p <0.00001）。对于两类分类（良性或恶性），FRCNN的准确性，敏感性和特异性分别为91.5％，83.3％和94.5％。BCD分别为86.6％，86.3％和86.6％；TRN分别为85.3％，83.5％和85.9％。FRCNN的假阳性率和阳性预测值分别为5.5％和84.7％，BCD分别为13.4％和70.5％，TRN分别为14.1％和68.5％。我们将FRCNN的分类性能与20位皮肤科医生进行了比较。结果，FRCNN的分类准确度优于皮肤科医生。将来，我们计划在社会上实施此系统，并使其被大众使用，以改善皮肤癌的预后。