Advanced Cancer Classification Using CNN-Transformer Integration on High-Resolution Histopathological Images

This research introduces an innovative approach for classifying lung and colon cancer using high-dimensional histopathological images, pushing forward the current advancements in medical image analysis. Our approach utilizes a newly designed multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for enhanced performance and Transformer architectures, allowing for more efficient feature extraction from complex medical images. The dataset used comprises 25,000 high-resolution images across six distinct cancer subtypes, further intensifying the challenges of computational efficiency and classification accuracy. Our approach identifies detailed patterns and correlations in the data by combining CNNs' localized feature extraction capabilities with Transformers' global context-awareness, which is critical for precision cancer detection. Additionally, we employ a novel attention-based feature selection mechanism, reducing dimensionality while preserving critical information, which significantly enhances computational efficiency. The proposed model incorporates adaptive learning rate strategies and ensemble learning techniques to improve robustness and prevent overfitting, leading to a more generalized and accurate classification. Our methodology achieves 99.5% classification accuracy, outperforming existing methods and demonstrating its suitability for handling complicated, high-dimensional datasets. This work establishes a new standard in cancer classification by introducing a robust and efficient method for medical image analysis.