Enhancing polyp characterization in colon capsule endoscopy using ResNet9-KAN

• This paper aims to enhance the polyp characterization performance within CCE images. Towards this, we introduce a novel AI architecture called ResNet9-KAN, which integrates Kolmogorov-Arnold network into the well-established ResNet9 architecture. • Our proposed ResNet-KAN was evaluated on a new CCE dataset generated using the PillCam Colon 2 system, focusing on a bowel cancer screening population aged 50-74 with a positive fecal immunochemical test. The CCEs were conducted in four outpatient clinics, with subsequent colonoscopies performed in four hospitals in the Region of Southern Denmark: Odense University Hospital, Lillebaelt Hospital, Esbjerg and Grindsted Hospital, and Hospital Sønderjylland. • Our proposed ResNet9-KAN achieved better accuracy, sensitivity, and specificity compared to state-of-the-art AI models such as ResNet101 and Google’s ViT-B-16, while requiring fewer computational resources, paving the way for more accurate and reliable diagnostic practices in clinical settings. Background and Aim: Colon capsule endoscopy (CCE) offers a minimally invasive method for imaging gastrointestinal lesions, including colorectal polyps, which may be precursors to colorectal cancer. However, its low image quality poses challenges for tasks such as polyp characterization. This work develops a low-complexity AI model, ResNet9-KAN, by integrating the Kolmogorov-Arnold network (KAN) into 9-layer residual network (ResNet9) architecture. This model efficiently characterizes polyps as neoplastic or non-neoplastic in CCE images, facilitating real-time patient management. Methods: This work utilized a CCE dataset generated from the PillCam Colon 2 system at four hospitals in the Region of Southern Denmark. It comprises 2089 CCE images of 479 polyps (317 neoplastic, 162 non-neoplastic) from a bowel cancer screening population aged 50 to 74. The proposed ResNet9-KAN and several existing AI models were trained on 1672 CCE images (221 neoplastic, 113 non-neoplastic polyps) and evaluated on 569 test images (48 neoplastic, 25 non-neoplastic polyps). Results: The evaluation revealed that our proposed ResNet9-KAN surpassed existing AI models with per-image characterization accuracy of 97.71 %, demonstrating an excellent balance between sensitivity (97.10 %) and specificity (98.17 %). It also achieved the highest F1 score of 0.9730 and a competitive area under the curve (AUC) of 0.9895. Additionally, ResNet9-KAN exhibited per-polyp characterization accuracy of 99.23 %, with a sensitivity of 99.85 %, specificity of 98.65 %, and an F1 score of 0.9912. Conclusions: This work highlights the efficacy of ResNet9-KAN in accurately characterizing polyps in low-quality CCE images, showing substantial potential for in situ characterization where histological verification currently requires a follow-up colonoscopy.