A deep learning network for breast mass detection using paired view mammogram

Jae Won Seo; Young Jae Kim; Chang Min Park; Kwang Nam Jin; Kwang Gi Kim

doi:10.1117/12.3006715

2 April 2024 A deep learning network for breast mass detection using paired view mammogram

Jae Won Seo, Young Jae Kim, Chang Min Park, Kwang Nam Jin, Kwang Gi Kim

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262P (2024) https://doi.org/10.1117/12.3006715
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Breast masses are the most critical clinical symptom of breast cancer, underscoring the importance of early detection for accurate diagnosis and the need for improved accuracy based on high detection rates and low false-positive rates. In a standard routine mammography screening, cranial-caudal (CC) and mediolateral-oblique (MLO) views are acquired per breast. Employing the two standard views aids radiologists in making more dependable decisions compared to relying on a single view, as it offers information on correspondence, thus enhancing reliability. As a result, this research introduces a deep learning model, the Paired-mammogram view Network, based on a convolutional neural network (CNN) that simultaneously utilizes both CC view and MLO view in mammography to improve the performance of breast mass detection. To assess the efficacy of the suggested approach, we conducted a performance comparison between the proposed method and both single and paired view models using the identical dataset. The proposed network based on Resnet50 reached a sensitivity of 0.922, precision of 0.884, and false positives per image of 0.156; The contrast single view model reached a sensitivity of 0.888, precision of 0.853, and false positives per image of 0.188. This work demonstrates the proposed algorithm based on a clinical approach can be utilized for early diagnosis of breast cancer.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jae Won Seo, Young Jae Kim, Chang Min Park, Kwang Nam Jin, and Kwang Gi Kim "A deep learning network for breast mass detection using paired view mammogram", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262P (2 April 2024); https://doi.org/10.1117/12.3006715

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