Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
نویسندگان
چکیده
We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data 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 fertile which apply techniques. Breast cancer one most common cancers leading cause death around world. Early detection critical treating disease effectively. density plays significant role determining likelihood risk breast cancer. describes amount fibrous glandular tissue compared with fatty breast. categorized using system called ACR BI-RADS. The assigns four classes. In class A, breasts almost entirely fatty. B, scattered areas fibroglandular appear breasts. C, heterogeneously dense. D, extremely This paper applies pre-trained Convolutional Neural Network (CNN) on local mammogram dataset classify density. Several transfer learning models were tested consisting more 800 screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 2B0, Xception gave highest accuracy both four- two-class classification. To enhance classification, we applied weighted average ensembles, performance was visibly improved. overall classification ensembles 78.11%.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12115599