Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, many people use a computer-aided polyp classification technique to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. This article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The network then reduces dimensions using learnable 3×3 convolutional kernels to generate output channels. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset). A n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96.4%, and 94.8% accuracy in two-class polyp classification (positive and negative). In the three-class classification, correct classification rates of 96.2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96.7% with 93.1% specificity. Furthermore, the study compared the performance of the state-of-the-art general classification models and commonly used CNNs. Six end-to-end models based on CNNs were trained using 2 dataset of video sequences. The experimental results demonstrate that the proposed ESWCNN method can effectively classify polyps with higher accuracy and efficacy compared to the state-of-the-art CNN models. These findings can provide guidance for future research in polyp classification.

The authors have declared no competing interest.

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We have obtained ethical committee approval, from Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine，Approval Letter No.: Ethics Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine BE2020-082-01, Review date: First review: April 30,2020 Review: June 15,2020 All patient data has been analyzed anonymously, and no patient names or other private information will appear in the paper.

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1,UCI public dataset: Mesejo, P., Pizarro, D., Abergel, A., Rouquette, O., Beorchia, S., Poincloux, L., Bartoli, A. (2016). Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Transactions on Medical Imaging. http://www.depeca.uah.es/colonoscopy_dataset/ 2, PloyGen data set Ali S, Dmitrieva M, Ghatwary N, Bano S, Polat G, Temizel A, et al. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 2021:102002. https://www.synapse.org/#!Synapse:syn45200214 3,GDZY Data cannot be shared publicly because of Ethics. Data are available from the the Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine(TCM) Institutional Data Access / Ethics Committee.

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