Image Recognition of Small Intestinal Ulcer Based on MobileNetV2
Ulcer lesions under enteroscopy are complex in shape and difficult to differentiate and diagnose.To realize the artificial intelligence-assisted recognition of small intestinal ulcer lesions and improve the diagnosis efficiency and accuracy,a small intestinal ulcer lesion recognition algorithm based on the MobileNetV2 network was constructed.The MobileNetV2 was used as the backbone feature extraction network,and the output feature map was extracted in space at multiple scales and theninput to the channel attention module for feature recalibration,and the features on multiple scales were fused and output classification,in order to alleviate the impact of data imbalance,an improved loss function was proposed.The data set used were collected from a total of 2124 enteroscopy clinical images of 282 patients in Shanghai Changhai Hospital.The proposed method was used to test the data set,and the recognition accuracy was 87.86％,the average accuracy of 5-fold cross-validation was 87.24％.The gradient weighted class activation map was used for visual verification.At the same time,the proposed modules were applied to different backbone network architectures,which reached an improvement to a certain extent,and displayed good versatility.Experimental results showed that the network model extracted more information of lesion,strengthened the identification of lesion characteristics,and had a higher recognition accuracy for small intestinal ulcer images,and was able to initially realize the automatic identification of small intestinal ulcer types.
medical image classificationsmall intestine ulcerMobileNetV2spatial multi-scale featuresfeature recalibration
NETL
NSTL
万方数据
