Affiliation:
1. Department of Stomatology The Second Affiliated Hospital of Guangzhou Medical University Guangzhou China
2. College of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China
3. Center for Learning Sciences and Technologies The Chinese University of Hong Kong Shatin Hong Kong China
4. School of Basic Medical Sciences Guangzhou Medical University Guangzhou China
Abstract
AbstractAccurate clinical diagnosis of the stage of tumor development is essential for formulating a treatment plan. However, the stage of tongue tumor development among malignant, benign and leukoplakia is easily misdiagnosed, resulting differing treatment approaches, taking patients in danger and preventing them from receiving the deserved care. This study aimed to establish an automatic recognition system for tongue tumors at different stages of development using artificial intelligence methods along with pathological tissue section images. By improving Swin Transformer framework, a new framework in deep learning, the tissue slice image is used to identify the lesion and non‐lesion areas by the patch‐based method, and then an output is reconstructed by self‐Assembly method that the lesion areas is marked with heat map. Subsequently, an automatic recognition system with a friendly page is designed for the stage of tongue tumor development (malignant, benign, and leukoplakia). The proposed model has a high recognition accuracy (98.45%). The prediction accuracy of each category of the system is higher than that of specialist doctors with 13 years of experience. The Swin Transformer framework was improved in this study to accurately and automatically identify the various stages of tongue tumor development.
Funder
National Natural Science Foundation of China
Subject
General Engineering,General Computer Science
Cited by
2 articles.
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