Department of Physics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Department of Health Physics and Diagnostic Sciences, University of Nevada, Las Vegas, United States of America.
Biomed Phys Eng Express. 2024 Apr 18;10(3). doi: 10.1088/2057-1976/ad3cdf.
A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.
许多欠发达国家，特别是非洲国家，面临着与癌症相关的致命疾病的困扰。尤其是在女性中，由于无知和诊断延误，乳腺癌的发病率正日益上升。只有在癌症发展的早期阶段正确识别和诊断，才能有效地进行治疗。借助计算机辅助诊断和医学图像分析技术，可以加速和自动化癌症分类。本研究提出了利用 Residual Network 18（ResNet18）和 Residual Network 34（ResNet34）架构的迁移学习来检测乳腺癌。该研究探讨了如何利用 ResNet18 和 ResNet34 的迁移学习来识别乳腺 X 光照片中的乳腺癌，并使用具有最佳验证准确性的训练模型为放射科医生开发了一个演示应用程序。该研究使用了来自国家放射学会（NRS）档案的 200 套乳腺 X 射线摄影图像数据集。为了提高 X 射线乳腺摄影图像分类的一致性并生成更好的特征，数据集被归类为植入物阴性癌症、植入物阳性癌症、癌症阴性和癌症阳性。对于图像的多类分类，该研究对良性或恶性癌症病例的二进制分类的平均准确率为 86.7%，ResNet34 的验证准确率为 92%，ResNet18 的验证准确率为 92%。已经创建了一个展示 ResNet18 性能的原型 Web 应用程序。所获得的结果表明，迁移学习如何提高乳腺癌检测的准确性，为医疗专业人员提供了宝贵的帮助，特别是在非洲的情况下。
