To explore the construction of automatic classification model for the diagnosis of colorectal polyp based on deep learning.

From January 2018 to January 2023, 957 colonoscopy images were collected at 3 endoscopy centers in Suzhou (537 at Changshu NO.1 People′s Hospital, 359 at Changshu Hospital of Traditional Chinese Medicine, and 61 at the First Affiliated Hospital of Soochow University), by using various image enhanced endoscopy (IEE) techniques. Based on pathological features, these images were classified into normal group, hyperplastic polyps group, and adenomatous polyps group. By using the DenseNet-121, EfficientNet, ResNet101, and ResNet50 convolutional neural network (CNN) frameworks, deep learning models were constructed and tested against the performance of endoscopists with varied experience levels in terms of accuracy, recall rate, precision, F1 score, and time taken to read images.

EfficientNet outperformed the other models, with a 0.961 accuracy, 0.968 recall rate, 0.959 precision, and 0.962 F1 score. In terms of image reading time, all models significantly outperformed endoscopists, completing automatic diagnostic tasks in an average time of (4.08±0.63) seconds compared to the average time of (291.10±17.68) seconds required by endoscopists, showing a statistical difference (t=-36.22, P<0.01). The EfficientNet pretrained model, after transfer learning, was named " EffiPolypNet". It misclassified a few adenomatous polyps but achieved an accuracy of 0.90 and an AUC of 0.98. Visualization using t-distributed stochastic neighbor embedding (t-SNE) revealed semantic feature overlaps between adenomatous and hyperplastic polyps, which could account for the misclassifications. Gradient-weighted class activation mapping (Grad-CAM) and Shapley additive explanations (SHAP) elucidated the key image regions and relative importance of features in the model′s decision-making process.

The EffiPolypNet model outperformed other IEE techniques in categorizing the nature of colorectal polyps, offering efficient and dependable support for optical diagnosis in colonoscopy.