Deep learning-based classification of colonoscopic images using an attention-enhanced ConvNeXt V2 architecture.

Introduction: Proper interpretation of the colonoscopic images is important to early detect and diagnose colorectal diseases like polyps and inflammatory bowel diseases. However, the complex visual patterns and high intra-class similarity of such images make the automated classification task challenging. Methods: In this work, we propose an attention enhanced deep learning framework using ConvNeXt V2 for robust multi-class classification of colonoscopic images. The proposed method employs a Convolutional Block Attention Module (CBAM) in ConvNeXt V2 architecture to improve the feature representation by emphasizing the diagnostically relevant regions and ignoring the irrelevant background information. We used a balanced dataset of three classes: cecum (normal), polyp and ulcerative colitis with a uniform spatial resolution of 720 × 576 pixels. To improve the generalization of the model, we performed data augmentation for the training. The performance of the proposed model was extensively evaluated using 5-fold stratified cross-validation. Results: Experimental results show that the proposed approach achieves a mean classification accuracy of about 95% which is significantly better than the baseline ConvNeXt V2 model which achieved about 90% accuracy. Furthermore, the proposed model achieved a mean precision of 95.1% and an F1-score of 94.9%, which shows a reliable classification of all classes. Moreover, qualitative analysis by attention visualization reveals that the model can focus on clinically relevant areas related to pathological features. Discussion: The results demonstrated the effectiveness of modern convolutional architectures with embedded attention mechanisms in improving diagnostic performance in the analysis of colonoscopic images. The proposed framework provides a powerful and efficient tool for automatic classification of colorectal diseases and can assist clinicians for decision making.