Tri-ResNet: A Parallel Multi-Branch and Transfer Learning Triple-Input Model for Breast Cancer Detection and Classification

Breast cancer remains a leading cause of cancer-related deaths among women, highlighting the need for accurate computer-aided diagnosis systems (CADs). Convolutional neural networks (CNNs) have demonstrated substantial progress in medical image analysis, significantly improving diagnostic accuracy. This paper introduces Tri-ResNet, a triple-input model composed of parallel fine-tuned ResNet-based branches using transfer learning (TL) for efficient breast cancer classification. The model simultaneously processes full mammogram images (FMs), regions of interest images (ROIs), and contrast-enhanced ROI images (CLAHE-enhanced ROIs) using Contrast-Limited Adaptive Histogram Equalization (CLAHE). Extensive experiments were conducted using multiple pre-trained models across single-input and multi-input architectures. Tri-ResNet achieved outstanding results on the Mini-DDSM, MIAS, and INbreast datasets, with peak performance on MIAS reaching 99.62% accuracy for normal–abnormal classification and 99.14% for benign–malignant classification, while maintaining competitive results on Mini-DDSM and INbreast. The model consistently outperformed single-input models and state-of-the-art approaches, demonstrating the effectiveness of multi-input CNNs for enhancing automated breast cancer diagnosis.