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An improved EfficientNet model and its applications in pneumonia image classification
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Li, Linan, Tan, Zexuan and Han, Xiangzhu (2022) An improved EfficientNet model and its applications in pneumonia image classification. Journal of Engineering Science and Technology Review, 15 (6). pp. 49-54. doi:10.25103/jestr.156.07 ISSN 1791-9320.
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Official URL: http://dx.doi.org/10.25103/jestr.156.07
Abstract
Since the outburst of COVID-19, the medical system has been facing great challenges due to the explosive growth in detection and treatment needs within a short period. To improve the working efficiency of doctors, an improved EfficientNet model of Convolutional Neural Network (CNN) was proposed and applied for the diagnosis of pneumonia cases and the classification of relevant images in the present study. First, the acquired images of pneumonia cases were divided to determine the zones with target features, and image size was limited to improve the training speed of the network. Meanwhile, reinforcement learning was performed to the input dataset to further improve the training effect of the model. Second, the preprocessed images were inputted into the improved EfficientNet-B4 model. The depth and width of the model, as well as the resolution of the input images, were determined by optimizing the combination coefficient. On this basis, the model was scaled, and then its ability in extracting the features of deep-layer images was strengthened by introducing the attention mechanism. Third, the learning rate was adjusted by using the Adaptive Momentum (ADAM), and the training efficiency of the model was accelerated. Finally, the test set was inputted into the trained model. Results demonstrate that the improved model could detect 98% of patients with pneumonia and 97% of patients without pneumonia. The accuracy rate, precision rate, and sensitivity of the model were generally improved. Lastly, the training and test results of VGGNet, SqueezeNet-Elus, SqueezeNet-Relu, and the improved EfficientNet-B4 models were compared and evaluated. The improved EfficientNet-B4 model achieved the highest comprehensive accuracy rate, reaching 92.95%. The proposed method provides some references to the application of the CNN model in image classification and recognition.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Image processing -- Digital techniques, Deep learning (Machine learning), Neural networks (Computer science) , Diagnostic imaging -- Data processing , Pneumonia -- Diagnosis | ||||||
Journal or Publication Title: | Journal of Engineering Science and Technology Review | ||||||
Publisher: | JESTR | ||||||
ISSN: | 1791-9320 | ||||||
Official Date: | 26 December 2022 | ||||||
Dates: |
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Volume: | 15 | ||||||
Number: | 6 | ||||||
Page Range: | pp. 49-54 | ||||||
DOI: | 10.25103/jestr.156.07 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 21 March 2023 | ||||||
Date of first compliant Open Access: | 22 March 2023 | ||||||
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