Classification of BI-RADS using convolutional neural network and effecientNet-B7

From its development, image processing technology has become increasingly complex, such as detecting cancer in an image. In this context, the American College of Radiology (ACR) has developed a standard format and terminology called the breast imaging reporting and data system (BI-RADS). The aim of this research is to determine whether CNN can classify BI-RADS types (C1, C2, C3, C4), the best model, and the classification results of BI-RADS types on mammograms using CNN, and the best values for accuracy, sensitivity, specificity, and precision. Google Colaboratory and machine learning libraries were used in this research to create machine learning for BI-RADS classification. Then, a dataset from Kaggle titled "Mini-DDSM: Mammography-based Automatic Age Estimation" by Lekamlage et al., from the Association for Computing Machinery (ACM), Kyoto, Japan, was utilized. Based on the research, an accuracy of 85.9% was achieved for Category 1 classification. Category 2 classification attained an accuracy of 77.9%. The accuracy for Category 3 classification was 81.4%, and for Category 4, it reached 90.8%.