| dc.description.abstract | Colorectal cancer is one of the most common and deadliest malignancies worldwide. According to the latest data from the National Health Agency′s Cancer Registry, the incidence of colorectal cancer in Taiwan has been the highest in the world for 15 consecutive years, with an average increase of approximately 15,000~16,000 newly diagnosed patients each year. The development of colorectal cancer is closely related to the formation of polyps. Therefore, early detection and polypectomy through endoscopic examinations can effectively prevent the occurrence of colorectal cancer. For endoscopists, the ability to accurately differentiate colonic polyp types and make informed treatment decisions during examinations is crucial.
Image-enhanced endoscopic techniques, including chromoendoscopy and narrow-band imaging, are used to enhance the surface patterns or microvascular appearance of polyps. These enhanced visual features, such as pigment distribution, vascular density, lesion extent, and protrusion degree, can significantly improve the diagnostic capabilities of endoscopists in identifying colonic polyp types. However, the widespread use of narrow-band imaging endoscopy may be limited in certain clinical settings due to cost considerations.
To provide endoscopists with lower equipment costs and effective evaluations, and to address the limited predictive capabilities of convolutional neural network (CNN) models caused by the blurred texture features in white-light polyp imaging, this study proposes a novel input feature representation method. This method combines texture features, spectral features, and high-boost filters for multi-class classification of colonic polyps based on white-light imaging. Additionally, a new model architecture called CBAMNet is introduced, which further enhances the accuracy of white-light polyp classification by combining CBAM with other well-known normalization techniques.
Experimental results demonstrate that the texture features proposed in this study have a significant impact on the classification performance of CNN in white-light polyp images (p < 0.05). Through 5-fold cross-validation on the test data, our proposed features improve the average F1 score of six state-of-the-art CNN models by 14.0%. By combining our method and model, we successfully increase the multi-class colonic polyp classification F1 score to 87.0% under white-light conditions. The research findings highlight the importance of combining texture features to improve the accuracy of white-light colonic polyp imaging classification, as well as the classification potential of CBAMNet. | en_US |