Construction of automatic classification model for colorectal polyp diagnosis based on deep learning
Chen Jian1, Zhang Zihao2, Lu Yongda3, Xia Kaijian4, Wang Ganhong5, Liu Luojie1, Xu Xiaodan1
1Department of Gastroenterology, Changshu NO.1 People′s Hospital (Affiliated Changshu Hospital of Soochow University), Changshu 215500, China; 2Shanghai Hao Brothers Educational Technology Co., Ltd., Shanghai 200434, China; 3Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou 215006, China; 4Changshu Key Laboratory of Medical Artificial Intelligence and Big Data, Changshu 215500, China; 5Department of Gastroenterology, Changshu Traditional Chinese Medicine Hospital (New District Hospital), Changshu 215500, China
Abstract：ObjectiveTo explore the construction of automatic classification model for the diagnosis of colorectal polyp based on deep learning. MethodsFrom 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. ResultsEfficientNet 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. ConclusionThe EffiPolypNet model outperformed other IEE techniques in categorizing the nature of colorectal polyps, offering efficient and dependable support for optical diagnosis in colonoscopy.
陈健 张子豪 卢勇达 夏开建 王甘红 刘罗杰 徐晓丹. 基于深度学习构建结直肠息肉诊断
自动分类模型[J]. 中华诊断学电子杂志, 2024, 12(1): 9-17.
Chen Jian, Zhang Zihao, Lu Yongda, Xia Kaijian, Wang Ganhong, Liu Luojie, Xu Xiaodan. Construction of automatic classification model for colorectal polyp diagnosis based on deep learning. zhzdx, 2024, 12(1): 9-17.
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