Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models  

作　　者：Zhi-Guo Xiao Xian-Qing Chen Dong Zhang Xin-Yuan Li Wen-Xin Dai Wen-Hui Liang 

机构地区：[1]School of Computer Science Technology,Changchun University,Changchun 130022,Jilin Province,China [2]School of Computer Science Technology,Beijing Institute of Technology,Beijing 100811,China

出　　处：《World Journal of Gastroenterology》2024年第48期5111-5129,共19页世界胃肠病学杂志（英文）

基　　金：Supported by The Science and Technology Development Center of The Ministry of Education,No.2022BC004。

摘　　要：BACKGROUND Wireless capsule endoscopy(WCE)has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology.However,the complexity of the digestive tract structure,and the diversity of lesion types,results in different sites and types of lesions distinctly appearing in the images,posing a challenge for the accurate identification of digestive tract diseases.AIM To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions,thereby improving the diagnostic efficiency of doctors,and creating significant clinical application value.METHODS In this paper,we propose a neural network model,WCE_Detection,for the accurate detection and classification of 23 classes of digestive tract lesion images.First,since multicategory lesion images exhibit various shapes and scales,a multidetection head strategy is adopted in the object detection network to increase the model's robustness for multiscale lesion detection.Moreover,a bidirectional feature pyramid network(BiFPN)is introduced,which effectively fuses shallow semantic features by adding skip connections,significantly reducing the detection error rate.On the basis of the above,we utilize the Swin Transformer with its unique self-attention mechanism and hierarchical structure in conjunction with the BiFPN feature fusion technique to enhance the feature representation of multicategory lesion images.RESULTS The model constructed in this study achieved an mAP50 of 91.5%for detecting 23 lesions.More than eleven single-category lesions achieved an mAP50 of over 99.4%,and more than twenty lesions had an mAP50 value of over 80%.These results indicate that the model outperforms other state-of-the-art models in the end-to-end integrated detection of human digestive tract lesion images.CONCLUSION The deep learning-based object detection network detects multiple digestive tract lesions in WCE images with high accuracy,improving t

关 键 词：Human digestive tract Artificial intelligence Deep learning Wireless capsule endoscopy Object detection 

分 类 号：R735[医药卫生—肿瘤]