Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography

被引:0
作者
Lin, Kuo-Hsuan [1 ,2 ]
Lu, Nan-Han [3 ,4 ,5 ]
Okamoto, Takahide [6 ]
Huang, Yung-Hui [5 ]
Liu, Kuo-Ying [4 ,5 ]
Matsushima, Akari [6 ]
Chang, Che-Cheng [7 ]
Chen, Tai-Been [5 ,8 ]
机构
[1] I Shou Univ, Dept Informat Engn, Kaohsiung 82445, Taiwan
[2] I Shou Univ, E Da Hosp, Dept Emergency Med, Kaohsiung 82445, Taiwan
[3] Tajen Univ, Dept Pharm, Pingtung 90741, Taiwan
[4] I Shou Univ, E Da Canc Hosp, Dept Radiol, 1 Yida Rd, Kaohsiung 82445, Taiwan
[5] I Shou Univ, Dept Med Imaging & Radiol Sci, Kaohsiung 82445, Taiwan
[6] Teikyo Univ, Fac Med Technol, Dept Radiol Technol, Tokyo 1738605, Japan
[7] I Shou Univ, E Da Hosp, Dept Radiol, Kaohsiung 82445, Taiwan
[8] Natl Yang Ming Chiao Tung Univ, Inst Stat, Hsinchu 30010, Taiwan
关键词
CXR; COVID-19; bacterial pneumonia; hybrid AI model; deep learning; feature fusion; support vector machine;
D O I
10.3390/healthcare11101367
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.
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页数:15
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