Optimizing Deep Learning Architectures for Enhanced Breast Cancer Detection on Mammography Images

Abstract

Breast cancer is a major health issue affecting millions of women globally, and early detection through mammography is critical for improving survival rates. However, mammography often faces challenges, such as imbalanced datasets and poor image quality, especially in dense breast tissue, which complicates accurate detection. This project explores the use of deep learning techniques, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), to address these challenges and enhance breast cancer detection. Five models—ResNet50V2, MobileNetV2, VGG16, ResNet from scratch, and ViT—were compared using various evaluation metrics. Two datasets, RSNA and MIAS, were used, with preprocessing applied only to the RSNA dataset. The experiments were divided into three stages: the first stage evaluated the original RSNA dataset without preprocessing, the second stage tested the balanced and preprocessed RSNA dataset with and without data augmentation, and the third stage applied similar experiments on the MIAS dataset. The results showed that preprocessing and balancing the RSNA dataset significantly improved model performance, while data augmentation further enhanced accuracy and generalization. ViT models outperformed other CNN architectures, demonstrating superior detection abilities after augmentation. ResNet from scratch also showed strong results, benefiting from its controlled architecture that adapted well to high-resolution images. This study highlights how addressing class imbalance and optimising model architectures can lead to more effective breast cancer detection using deep learning.