Ensemble-Based Deep Learning for Breast Cancer Detection and Classification in Histopathological Images

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being crucial for improved patient outcomes. This paper presents a comprehensive deep learning framework for automated breast cancer detection in histopathological images, incorporating advanced preprocessing techniques, enhanced segmentation methods, and multi-architecture ensemble classification. Our methodology employs a systematic approach using the BreakHis dataset with rigorous experimental design to ensure unbiased evaluation. The framework integrates Fast Non-Local Means denoising, Wiener filtering, and U-Net based segmentation for optimal image preprocessing, followed by feature extraction from multiple categories including statistical, texture, and morphological features. We evaluate 18 state-of-the-art convolutional neural network architectures and implement advanced ensemble methods for superior classification performance. Our results demonstrate exceptional performance with the best individual model achieving 98.90% accuracy, while ensemble methods reach 99.45% accuracy through confidence-based fusion. The framework provides comprehensive interpretability through Grad-CAM visualizations and statistical validation using McNemars test and medical diagnostic metrics. This work represents a significant advancement in computational pathology, offering a robust and clinically viable solution for automated breast cancer diagnosis with enhanced accuracy and reliability.