Citation: Justaniah, E.; Aldabbagh,
G.; Alhothali, A.; Abourokbah, N.
Classifying Breast Density from
Mammogram with Pretrained CNNs
and Weighted Average Ensembles.
Appl. Sci. 2022, 12, 5599. https://
doi.org/10.3390/app12115599
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 9 April 2022
Accepted: 23 May 2022
Published: 31 May 2022
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Article
Classifying Breast Density from Mammogram with Pretrained
CNNs and Weighted Average Ensembles
Eman Justaniah
1,
*
, Ghadah Aldabbagh
1
, Areej Alhothali
1
and Nesreen Abourokbah
2
1
Department of Computer Science, Faculty of Computing and Information Technology,
King Abdulaziz University, Jeddah 22254, Saudi Arabia; galdabbagh@kau.edu.sa (G.A.);
aalhothali@kau.edu.sa (A.A.)
2
Consultant Radiologist, King Abdulaziz Medical City, Jeddah 11426, Saudi Arabia;
abourokbahne@ngha.med.sa
* Correspondence: ejustaniah0001@stu.kau.edu.sa
Abstract:
We are currently experiencing a revolution in data production and artificial intelligence (AI)
applications. Data are produced much faster than they can be consumed. Thus, there is an urgent
need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile
field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading
cause of death around the world. Early detection is critical to treating the disease effectively. Breast
density plays a significant role in determining the likelihood and risk of breast cancer. Breast density
describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the
breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast
density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of
fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class
D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network
(CNN) on a local mammogram dataset to classify breast density. Several transfer learning models
were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz
Medical City (KAMC). Inception V3, EfficientNet 2B0, and Xception gave the highest accuracy for
both four- and two-class classification. To enhance the accuracy of density classification, we applied
weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR
classification with weighted average ensembles was 78.11%.
Keywords: deep learning; mammography; breast density; breast cancer; CAD systems
1. Introduction
Artificial intelligence (AI) algorithms have improved and affected every aspect of
modern life. In the last two decades, machine learning techniques and computer power
have experienced momentous development, leading to an enormous increase in digital
data in different fields. These advances have enabled researchers to solve real-world
problems in many areas, including the medical image analysis field. Medical imaging
was first used in 1895 [
1
]. Since that time, the devices and quality of medical imaging
have continued to evolve. Today, medical imaging is an essential tool for diagnosing and
detecting diseases such as cancer [
2
]. Cancer is a significant public health concern that
affects people around the world, and it is the second leading cause of death in the US [
3
].
Breast cancer is one of the most prevalent cancers that affect women [
4
], but it can also arise
in men [
5
]. In Saudi Arabia, in 2018, breast cancer was the most common type of cancer,
with an incidence rate of 14.8% and a mortality rate of 8.5% [
6
]. In 2020, breast cancer
remained the most common type of cancer in Saudi Arabia. Breast cancer is a disease
caused by the abnormal growth of breast cells. Usually, somatic cells divide to produce
new cells in an orderly manner. When the cells of a specific part of the body grow and
divide out of control, a mass of tissue called a tumor is created. A tumor that consists
Appl. Sci. 2022, 12, 5599. https://doi.org/10.3390/app12115599 https://www.mdpi.com/journal/applsci
