Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis - Leong, Yew Sum; Hasikin, Khairunnisa; Lai, Khin Wee; Mohd Zain, Norita; Azizan, Muhammad Mokhzaini (Frontiers in Public Health, 2022)
Breast Cancer is one of the common cancers in women and may increase mortality risk if they were misdiagnosed and left untreated. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator of early breast cancer. However, due to their small size and are scattered in mammogram images, microcalcifications are often missed and wrongly classified during screening. 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 and 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 of 96.97%, and finally AlexNet of 83.06%.