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Enhancing Viral Pneumonia Diagnosis Accuracy Using Transfer Learning and Ensemble Technique from Chest X-ray Images
Chandrashekhar Uppin, Usman Bello Abubakar
Pages - 43 - 53     |    Revised - 30-06-2022     |    Published - 01-08-2023
Volume - 17   Issue - 3    |    Publication Date - August 2023  Table of Contents
Viral Pneumonia, Chest X-ray, Transfer Learning, Ensemble Methods, Medical Image Classification, Artificial Intelligence.
Pneumonia is an acute pulmonary infection that can be caused by bacteria, viruses, or fungi. It infects the lungs, causing inflammation of the air sacs and pleural effusion: a condition in which the lung is filled with fluid. The diagnosis of pneumonia is tasking as it requires a review of Chest X-ray (CXR) by specialists, laboratory tests, vital signs, and clinical history. Utilizing CXR is an important pneumonia diagnostic method for the evaluation of the airways, pulmonary parenchyma, and vessels, chest walls among others. It can also be used to show changes in the lungs caused by pneumonia. This study aims to employ transfer learning, and ensemble approach to help in the detection of viral pneumonia in chest radiographs. The transfer learning model used was Inception network, ResNet-50, and InceptionResNetv2. With the help of our research, we were able to show how well the ensemble technique, which uses InceptionResNetv2 and the utilization of the Non-local Means Denoising algorithm, works. By utilizing these techniques, we have significantly increased the accuracy of pneumonia classification, opening the door for better diagnostic abilities and patient care. For objective labeling, we obtained a selection of patient chest X-ray images. In this work, the model was assessed using state-of-the-art metrics such as accuracy, sensitivity, and specificity. From the statistical analysis and scikit learn python analysis, the accuracy of the ResNet-50 model was 84%, the accuracy of the inception model was 91% and lastly, the accuracy of the InceptionResNetv2 model was 96%.
Abubakar, U. B., Boukar, M. M., & Adeshina, S. (2022). Comparison of Transfer Learning Model Accuracy for Osteoporosis Classification on Knee Radiograph. 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings. https://doi.org/10.1109/ICMI55296.2022.9873731.
Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., Shasky, C., Zhang, Y. D., Tavares, J. M. R. S., & Raja, N. S. M. (2020). Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters, 129, 271–278. https://doi.org/10.1016/J.PATREC.2019.11.013.
Buades, A., Coll, B., & Morel, J.-M. (2011). Non-Local Means Denoising. Image Processing On Line, 1, 208–212. https://doi.org/10.5201/IPOL.2011.BCM_NLM.
Chandra, T. B., & Verma, K. (2020). Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm. Advances in Intelligent Systems and Computing, 1022 AISC, 21–33. https://doi.org/10.1007/978-981-32-9088-4_3/COVER.
InceptionResNetV2 Simple Introduction | by Zahra Elhamraoui | Medium. (n.d.). Retrieved June 13, 2023, from https://medium.com/@zahraelhamraoui1997/inceptionresnetv2-simple-introduction-9a2000edcdb6.
Jaiswal, A. K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A., & Rodrigues, J. J. P. C. (2019). Identifying pneumonia in chest X-rays: A deep learning approach. Measurement, 145, 511–518. https://doi.org/10.1016/J.MEASUREMENT.2019.05.076.
Kermany, D., Zhang, K., & Goldbaum, M. (2018). Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. 2. https://doi.org/10.17632/RSCBJBR9SJ.2.
Kundu, R., Das, R., Geem, Z. W., Han, G. T., & Sarkar, R. (2021). Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLOS ONE, 16(9), e0256630. https://doi.org/10.1371/JOURNAL.PONE.0256630
Kuo, K. M., Talley, P. C., Huang, C. H., & Cheng, L. C. (2019). Predicting hospital-acquired pneumonia among schizophrenic patients: A machine learning approach. BMC Medical Informatics and Decision Making, 19(1), 1–8. https://doi.org/10.1186/S12911-019-0792-1/TABLES/5
Li, Y., Zhang, Z., Dai, C., Dong, Q., &Badrigilan, S. (2020). Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis. Computers in Biology and Medicine, 123, 103898. https://doi.org/10.1016/J.COMPBIOMED.2020.103898
Manson, E., Ampoh, V. A., Fiagbedzi, E., Amuasi, J. H., Flether, J. J., & Schandorf, C. (2019). Curr Trends Clin Med Imaging Image Noise in Radiography and Tomography: Causes, Effects and Reduction Techniques. Current Trends in Clinical & Medical Imaging, 3(4), 86–91. https://doi.org/10.19080/CTCMI.2019.02.555620.
Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Abul Kashem, S. Bin, Islam, M. T., Al Maadeed, S., Zughaier, S. M., Khan, M. S., & Chowdhury, M. E. H. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in Biology and Medicine, 132, 104319. https://doi.org/10.1016/j.compbiomed.2021.104319.
Sharma, H., Jain, J. S., Bansal, P., & Gupta, S. (2020). Feature extraction and classification of chest X-ray images using CNN to detect pneumonia. Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, 227–231. https://doi.org/10.1109/CONFLUENCE47617.2020.9057809.
Wang, Y., Chen, Y., Yang, N., Zheng, L., Dey, N., Ashour, A. S., Rajinikanth, V., Tavares, J. M. R. S., & Shi, F. (2019). Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Applied Soft Computing, 74, 40–50. https://doi.org/10.1016/J.ASOC.2018.10.006.
Yang, Y., & Mei, G. (2022). Pneumonia Recognition by Deep Learning: A Comparative Investigation. Applied Sciences 2022, Vol. 12, Page 4334, 12(9), 4334. https://doi.org/10.3390/APP12094334.
Dr. Chandrashekhar Uppin
Department of Computer Science, Baze University Abuja - Nigeria
Dr. Usman Bello Abubakar
Department of Computer Science, Baze University Abuja - Nigeria

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