Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Biomedical Engineering, Center for Image and Signal Processing (CISIP), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Front Public Health. 2022 Apr 28;10:875305. doi: 10.3389/fpubh.2022.875305. eCollection 2022.
Breast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%.
乳腺癌是女性最常见的癌症类型之一，如果诊断错误或治疗延误，死亡率很高。乳腺癌患者常存在乳腺微钙化，这是乳腺癌早期的有效指标。然而，由于微钙化体积小，在乳腺 X 线图像中呈间接散射，在筛查中常被遗漏和错误分类。针对这一问题，本项目提出了一种自适应迁移学习深度卷积神经网络，用于对乳腺钙化病例的乳腺 X 线图像进行分割，以实现早期乳腺癌的诊断和干预。利用乳腺微钙化的乳腺 X 线图像对几个深度神经网络模型进行训练，并对其性能进行比较。对感兴趣区域图像进行图像滤波，以去除可能的伪影和噪声，从而提高图像质量，然后再进行训练。调整了不同的超参数，如 epoch、batch size 等，以获得最佳的结果。此外，还将所提出的 ResNet50 微调超参数的性能与另一种最先进的机器学习网络，如 ResNet34、VGG16 和 AlexNet 进行了比较。利用混淆矩阵进行比较。研究结果表明，所提出的 ResNet50 达到了最高的准确率，为 97.58%，其次是 ResNet34，为 97.35%，VGG16 为 96.97%，最后是 AlexNet，为 83.06%。
