College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, Liaoning, China.
Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Phys Eng Sci Med. 2023 Sep;46(3):995-1013. doi: 10.1007/s13246-023-01262-3. Epub 2023 May 17.
Breast and thyroid cancers are the two most common cancers among women worldwide. The early clinical diagnosis of breast and thyroid cancers often utilizes ultrasonography. Most of the ultrasound images of breast and thyroid cancer lack specificity, which reduces the accuracy of ultrasound clinical diagnosis. This study attempts to develop an effective convolutional neural network (E-CNN) for the classification of benign and malignant breast and thyroid tumors from ultrasound images. The 2-Dimension (2D) ultrasound images of 1052 breast tumors were collected, and 8245 2D tumor images were obtained from 76 thyroid cases. We performed tenfold cross-validation on breast and thyroid data, with a mean classification accuracy of 0.932 and 0.902, respectively. In addition, the proposed E-CNN was applied to classify and evaluate 9297 mixed images (breast and thyroid images). The mean classification accuracy was 0.875, and the mean area under the curve (AUC) was 0.955. Based on data in the same modality, we transferred the breast model to classify typical tumor images of 76 patients. The finetuning model achieved a mean classification accuracy of 0.945, and a mean AUC of 0.958. Meanwhile, the transfer thyroid model realized a mean classification accuracy of 0.932, and a mean AUC of 0.959, on 1052 breast tumor images. The experimental results demonstrate the ability of the E-CNN to learn the features and classify breast and thyroid tumors. Besides, it is promising to classify benign and malignant tumors from ultrasound images with the transfer model under the same modality.
乳腺癌和甲状腺癌是全球女性中最常见的两种癌症。乳腺癌和甲状腺癌的早期临床诊断通常采用超声检查。大多数乳腺癌和甲状腺癌的超声图像缺乏特异性，这降低了超声临床诊断的准确性。本研究尝试开发一种有效的卷积神经网络（E-CNN），用于对乳腺和甲状腺超声图像中的良性和恶性肿瘤进行分类。收集了 1052 例乳腺肿瘤的 2 维（2D）超声图像，并从 76 例甲状腺病例中获得了 8245 个 2D 肿瘤图像。我们对乳腺和甲状腺数据进行了 10 折交叉验证，平均分类准确率分别为 0.932 和 0.902。此外，还将所提出的 E-CNN 应用于 9297 个混合图像（乳腺和甲状腺图像）的分类和评估。平均分类准确率为 0.875，平均曲线下面积（AUC）为 0.955。基于同一模态的数据，我们将乳腺模型转移到 76 例典型肿瘤图像的分类。微调模型的平均分类准确率为 0.945，平均 AUC 为 0.958。同时，转移的甲状腺模型在 1052 个乳腺肿瘤图像上实现了 0.932 的平均分类准确率和 0.959 的平均 AUC。实验结果表明，E-CNN 具有学习特征和分类乳腺和甲状腺肿瘤的能力。此外，通过同一模态下的迁移模型，有望对超声图像中的良性和恶性肿瘤进行分类。
