Efficient Classification Model of Pneumonia Infection Based on Deep Transfer Learning and Chest X-Ray Images
DOI:
https://doi.org/10.33193/IJSER.1.1.2022.37Keywords:
Computer-aided diagnosis; Pneumonia, CNN; X-ray, EfficientNetV2B0, Deep learning, Transfer learningAbstract
Chest X-ray radiographic (CXR) imaging facilitates the early and precise diagnosis of lung disease. Combining CXR imaging with CNN and other artificial intelligence tools can expedite the diagnosis process. Because there are only a small number of clinical images that have been categorized, the most difficult challenge to overcome before these images can be reliably used in disease progression diagnosis is the automated classification of these images as positive or negative cases. A deep-learning approach was proposed to classify lung diseases on CXR images using a transfer learning technique based on the EfficientNetV2 model as the backbone to boost computer-aided diagnosis (CAD) performance reliability and efficiency. The proposed model is trained and tested on the two classifications, normal and pneumonia, based on images from three publicly available chest X-ray datasets. Using the curated dataset of posteroanterior (PA) chest view radiography images, the implemented model achieves an outstanding performance on the tested data represented by an average accuracy of 99.92%. Consequently, it outperforms the most recent classification techniques de-scribed in the literature.
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[35] B. Antin, J. Kravitz, and E. Martayan, “Detecting Pneumonia in Chest X-Rays with Supervised Learning.”
[36] P. K. Sethy and S. K. Behera, “Detection of Coronavirus Disease (COVID-19) Based on Deep Features,” Mar. 2020, doi: 10.20944/PREPRINTS202003.0300.V1.
[37] K. Zrar et al., “Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms,” https://doi.org/10.1117/12.2588672, vol. 11734, no. 12, pp. 99–110, Apr. 2021, doi: 10.1117/12.2588672.
[38] R. Kundu, P. K. Singh, M. Ferrara, A. Ahmadian, and R. Sarkar, “ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images,” Multimedia Tools and Applications, vol. 81, no. 1, pp. 31–50, Jan. 2022, doi: 10.1007/S11042-021-11319-8/TABLES/9.
[39] E. H. Houssein, Z. Abohashima, M. Elhoseny, and W. M. Mohamed, “Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images,” Journal of Computational Design and Engineering, vol. 9, no. 2, pp. 343–363, Apr. 2022, doi: 10.1093/JCDE/QWAC003.
[40] U. Sait et al., “Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays).,” vol. 3, 2021, doi: 10.17632/9XKHGTS2S6.3.
[41] “NIH Chest X-ray dataset | Cloud Healthcare API | Google Cloud.” https://cloud.google.com/healthcare-api/docs/resources/public-datasets/nih-chest (accessed May 30, 2022).
[42] “TBX 11 | Kaggle.” https://www.kaggle.com/datasets/usmanshams/tbx-11 (accessed May 30, 2022).
[43] “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” http://proceedings.mlr.press/v97/tan19a.html (accessed May 30, 2022).
[44] “EfficientNetV2: Smaller Models and Faster Training.” http://proceedings.mlr.press/v139/tan21a.html (accessed May 30, 2022).
[45] “Transfer learning and fine-tuning | TensorFlow Core.” https://www.tensorflow.org/guide/keras/transfer_learning (accessed May 30, 2022).
[46] “Lookahead Optimizer: k steps forward, 1 step back.” https://proceedings.neurips.cc/paper/2019/hash/90fd4f88f588ae64038134f1eeaa023f-Abstract.html (accessed May 30, 2022).
[47] L. Liu et al., “On the Variance of the Adaptive Learning Rate and Beyond,” Aug. 2019, doi: 10.48550/arxiv.1908.03265.
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2022-09-17
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Kassem, M., & Albaker, B. M. (2022). Efficient Classification Model of Pneumonia Infection Based on Deep Transfer Learning and Chest X-Ray Images. Al-Iraqia Journal for Scientific Engineering Research, 1(1), 58–67. https://doi.org/10.33193/IJSER.1.1.2022.37
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