Leong, Yew Sum and Hasikin, Khairunnisa and Lai, Khin Wee and Mohd Zain, Norita and Azizan, Muhammad Mokhzaini (2022) Microcalcification discrimination in mammography using deep convolutional neural network: towards rapid and early breast cancer diagnosis. Frontiers in public health, 10. DOI https://doi.org/10.3389/fpubh.2022.875305.
Full text not available from this repository.Abstract
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%.
| Item Type: | Article |
|---|---|
| Funders: | University Malaya Research Grant Faculty Programme [Grant No:RF010-2018A] |
| Uncontrolled Keywords: | Transfer learning; Region of Interest (ROI); Intervention; Machine learning; Artificial intelligence |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Oct 2023 02:41 |
| Last Modified: | 05 Oct 2023 02:41 |
| URI: | http://eprints.um.edu.my/id/eprint/42863 |
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