TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network

Mariam Busaleh, Muhammad Hussain (), Hatim A. Aboalsamh, Fazal-e-Amin and Sarah A. Al Sultan
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Mariam Busaleh: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Muhammad Hussain: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Hatim A. Aboalsamh: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Fazal-e-Amin: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Sarah A. Al Sultan: Department of Radiology, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh 12372, Saudi Arabia

Mathematics, 2022, vol. 10, issue 23, 1-16

Abstract: Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.

Keywords: breast density classification; mammography; craniocaudal (CC) view; mediolateral oblique (MLO) view; BI-RADS; convolutional neural network (CNN); loss function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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