QDeepColonNet: a quantum-based deep learning network for colorectal cancer classification using attention-driven DenseNet and shuffled dynamic local feature extraction network

Abstract

Colorectal Cancer (CRC) is one of the most common and severe types of cancer globally, affecting millions of people each year. It primarily develops from benign polyps in the colon or rectum, which can turn malignant if not detected and treated early, leading to serious health risks. Current diagnostic methods for CRC detection are primarily manual and require significant time, resources and expertise. This creates a pressing need for automated solutions that are both efficient and highly accurate. This research proposes a hybrid Deep Learning (DL) and Quantum Machine Learning (QML)-based system for CRC classification, designed to address these challenges using a dual-track approach. The proposed QDeepColonNet leverages DL for robust feature extraction, combining DenseNet with an Enhanced Feature Learnable Group Attention (EFLGA) block to capture both high and mid-level features. Additionally, it integrates the Shuffled Dynamic Local Feature Extraction Network (SDLFEN) with a Lightweight Multi-Kernel Convolution (LMKC) block to capture short-range dependencies. The concatenated feature maps from both tracks are further refined by Efficient Channel Attention (ECA), enhancing cross-channel interactions without increasing complexity. Finally, the refined features are classified using a QML-based classifier, which effectively handles intricate data and captures complex feature relationships. To the best of our understanding, this is the first study to incorporate a QML-based hybrid classification network CRC detection. The performance of the proposed QDeepColonNet surpassed several state-of-the-art DL models and achieved a classification accuracy of 98.92% when tested on the EBHI dataset.