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- W4385377029 abstract "Corneal ulcer is a common disease located in the eye. If not detected and treated in a timely manner, it is highly likely to cause irreversible damage to the patient’s eyes, and even lead to blindness. Traditional detection methods have drawbacks such as complex steps and painful inspection processes. So there is an urgent need to develop a fast, convenient, and accurate detection method. In this article, we propose an improved Mobile ViT framework for corneal ulcer classification. Our method is based on the original Mobile ViT model, but we integrated an attention module to enhance feature extraction functionality. In addition, we have improved the connection method of the Mobile ViT module by fusing local features with global features to improve feature extraction capabilities and information exchange between images. Subsequent experiments showed that our modifications were effective and feasible. By using these two improvement strategies, the accuracy was first in-creased from 88.7% to 90.8%, and then from 90.8% to 91.5%, while the number of parameters remained basically. Visualize the extracted feature map using Grad-CAM (Gradient-weighted Class Activation Mapping), and the results show that the extracted features exist in the ulcer area." @default.
- W4385377029 created "2023-07-30" @default.
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- W4385377029 date "2023-01-01" @default.
- W4385377029 modified "2023-09-27" @default.
- W4385377029 title "Corneal Ulcer Automatic Classification Network Based on Improved Mobile ViT" @default.
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- W4385377029 doi "https://doi.org/10.1007/978-981-99-4742-3_51" @default.
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