# Improving Normal/Abnormal and Benign/Malignant Classifications in Mammography with ROI-Stratified Deep Learning

## TL;DR

This paper shows that using region of interest (ROI) stratification in deep learning models improves accuracy in classifying mammograms as normal/abnormal and benign/malignant.

## Contribution

The novelty lies in stratifying mammography images by ROI presence to enhance classification accuracy in deep learning models.

## Key findings

- ROI-based stratification improved diagnostic accuracy for both normal–abnormal and benign–malignant classifications.
- The benefit of ROI stratification increases with larger dataset sizes.
- Multiple deep learning models showed improved performance with the proposed stratification method.

## Abstract

Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal–abnormal and benign–malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several DL models, including ResNet, EfficientNet, SwinTransformer, ConvNeXt, and MobileNet. For experimentation, we used the publicly available VinDr., CDD-CESM, and DMID datasets. Our comparison with prediction results obtained without ROI-based stratification demonstrated that the utility of considering ROI presence to enhance diagnostic accuracy in mammography increases along with the data volume. These findings support the usefulness of our stratification approach, particularly as a dataset’s size grows.