Department of Computer Science, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-Ok, Thailand.
Asian Pac J Cancer Prev. 2023 Mar 1;24(3):1081-1088. doi: 10.31557/APJCP.2023.24.3.1081.
This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of the radiologists and image quality are important to detect and diagnose the tumor using handheld ultrasound, the ability of this approach tends to assist the radiologist's decision for breast cancer diagnosis.
Breast ultrasound images were provided by the Department of Radiology of Thammasat University and Queen Sirikit Center of Breast Cancer of Thailand. The dataset consists of 655 images including 445 benign and 210 malignant. Several data augmentation methods including blur, flip vertical, flip horizontal, and noise have been applied to increase the training and testing dataset. The tumor detection, localization, and classification were performed by drawing the appropriate bounding box around it using YOLO7 architecture based on deep learning techniques. Then, the automatic tumor volume estimation was performed using a simple pixel per metric technique.
The model demonstrated excellent tumor detection performance with a confidence score of 0.95. In addition, the model yielded satisfactory predictions on the test sets, with a lesion classification accuracy of 95.07%, a sensitivity of 94.97%, a specificity of 95.24%, a PPV of 97.42%, and an NPV of 90.91%.
An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique yielded satisfactory predictions in distinguishing benign from malignant breast lesions. In addition, automatic tumor volume estimation was performed. Our approach could be integrated into the conventional breast ultrasound machine to assist the radiologist's decision for breast cancer diagnosis.
本研究旨在开发一种基于计算机分析乳腺超声图像的深度学习技术，实现自动乳腺肿瘤检测和分类，包括自动肿瘤体积估计。当使用手持式超声检测和诊断肿瘤时，放射科医生的技能水平和图像质量很重要，这种方法的能力倾向于辅助放射科医生进行乳腺癌诊断。
乳腺超声图像由泰国玛希隆大学放射科和泰国诗丽吉王后乳腺癌中心提供。数据集包括 655 张图像，其中 445 张为良性，210 张为恶性。应用了几种数据增强方法，包括模糊、垂直翻转、水平翻转和噪声，以增加训练和测试数据集。使用基于深度学习技术的 YOLO7 架构，通过在其周围绘制适当的边界框来进行肿瘤检测、定位和分类。然后，使用简单的像素度量技术进行自动肿瘤体积估计。
该模型在置信度评分为 0.95 时表现出优异的肿瘤检测性能。此外，该模型在测试集上也取得了令人满意的预测结果，病灶分类准确率为 95.07%，灵敏度为 94.97%，特异性为 95.24%，阳性预测值为 97.42%，阴性预测值为 90.91%。
使用深度学习技术的自动乳腺肿瘤检测和分类，包括自动肿瘤体积估计，在区分良性和恶性乳腺病变方面取得了令人满意的预测结果。此外，还进行了自动肿瘤体积估计。我们的方法可以集成到常规的乳腺超声机中，以辅助放射科医生进行乳腺癌诊断。
