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

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.

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.

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.

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.