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%.
There is a significant development in computeraided detection (CADe) and computer-aided diagnostic (CADx) systems in recent years. This development coincides with the evolution of computing power and the growth of data. The CAD systems support detections and diagnosis of significant diseases, including cancer. Breast cancer is one of the most prevalent cancers influencing women and causing death around the world. Early detection of breast cancer has a significant effect on treatment. The typical CAD system goes through various steps, including image segmentation, feature extraction, and image classification. Image segmentation plays an important role in CAD systems and simplifies further processing. This review explores popular mammogram segmentation techniques. A mammogram is medical imaging which uses a low-dose x-ray system to see inner tissues of the breast. There are many segmentation techniques used to segment medical images. These techniques can be categorized into five main categories: regionbased methods, boundary-based methods, atlas-based methods, model-based methods, and deep learning. A ground truth image is needed to measure the performance of the segmentation algorithm. Different performance measurements were used to evaluate the segmentation process, including accuracy, precision, recall, F1 score, Hausdorff Distance, Jaccard, and Dice Index. The research in mammogram segmentation has yielded promising results, but there is room for improvements.
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