Tongue image quality assessment based on a deep convolutional neural network
| العنوان: | Tongue image quality assessment based on a deep convolutional neural network |
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| المؤلفون: | Huang Jingbin, Xinghua Yao, Xuxiang Ma, Liping Tu, Jiang Tao, Jiatuo Xu, Qing-feng Wu, Xiaojuan Hu, Ji Cui |
| المصدر: | BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-14 (2021) |
| بيانات النشر: | BioMed Central, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Image quality, Computer applications to medicine. Medical informatics, R858-859.7, Decision tree, Health Informatics, 02 engineering and technology, Convolutional neural network, ResNet, Machine Learning, 03 medical and health sciences, Naive Bayes classifier, Tongue, 0202 electrical engineering, electronic engineering, information engineering, Humans, AdaBoost, 030304 developmental biology, 0303 health sciences, business.industry, Health Policy, Deep learning, DenseNet, Pattern recognition, Bayes Theorem, Computer Science Applications, Random forest, Support vector machine, Tongue diagnosis, 020201 artificial intelligence & image processing, Artificial intelligence, Neural Networks, Computer, business, Algorithms, Research Article, Quality assessment |
| الوصف: | Background Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM. Methods Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score. Results The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA. Conclusions Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible. |
| اللغة: | English |
| تدمد: | 1472-6947 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25eccd7973dc0a357b9e981a30eb6fc3 http://europepmc.org/articles/PMC8097848 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....25eccd7973dc0a357b9e981a30eb6fc3 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14726947 |
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