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

Frontiers in Public Health

article

Yew Sum Leong¹  Khairunnisa Hasikin¹  Khin Wee Lai¹  Norita Mohd Zain¹  Muhammad Mokhzaini Azizan³ 

[1] Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya;Department of Biomedical Engineering, Center for Image and Signal Processing ,(CISIP), Faculty of Engineering, Universiti Malaya;Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia

关键词: transfer learning;    region of interest (ROI);    intervention;    machine learning;    artificial intelligence;   

DOI  :  10.3389/fpubh.2022.875305

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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%.

【 授权许可】

CC BY