Publication:
Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

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dc.contributor.authorYew Sum Leongen_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorKhin Wee Laien_US
dc.contributor.authorNorita Mohd Zainen_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.date.accessioned2024-05-28T05:49:44Z
dc.date.available2024-05-28T05:49:44Z
dc.date.issued2022
dc.date.submitted2022-11-11
dc.descriptionFront. Public Health 10:875305.en_US
dc.description.abstractBreast 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%.en_US
dc.identifier.citationLeong YS, Hasikin K, Lai KW, Mohd Zain N and Azizan MM (2022) Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis. Front. Public Health 10:875305. doi: 10.3389/fpubh.2022.875305en_US
dc.identifier.doi10.3389/fpubh.2022.875305
dc.identifier.epage13
dc.identifier.issn2296-2565
dc.identifier.issue2022
dc.identifier.other2494-19
dc.identifier.spage1
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fpubh.2022.875305/full
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/6618
dc.identifier.volume2022
dc.language.isoenen_US
dc.publisherFRONTIERSen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.subjecttransfer learning, region of interest (ROI), intervention, machine learning, artificial intelligenceen_US
dc.titleMicrocalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosisen_US
dc.typeArticleen_US
dspace.entity.typePublication

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