Abstract
Pneumonia has been around from a very long time affecting lakhs of people across the globe. Now, with the pandemic COVID-19 around and pneumonia being one of the symptoms of COVID-19, it must be detected in a very early stage so that the person can be treated without any further complications. Chest X-ray images can be used to detect pneumonia in an easier and cheaper way. Hence, radiologists can use deep learning algorithms to diagnose pneumonia effectively. It’s very crucial to consider the fact that not every pneumonia is because of COVID-19, hence we are classifying the X-ray into three categories, i.e., bacterial pneumonia, viral pneumonia, and normal using transfer learning like VGG19, Xception, Densenet121, and InceptionV3. A Deep Learning model’s accuracy is highly dependent on the amount of training set. So, we implement a generative adversarial network (GAN) for data augmentation. We observed that Xception achieved the highest accuracy of 83% with a precision of 85%, recall of 83%, and F1 score of 83% at the end of 100th epoch.
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References
Almezhghwi K, Serte S, Al-Turjman F (2021) Convolutional neural networks for the classification of chest X-rays in the IoT era. Multimed Tools Appl 80:29051–29065. https://doi.org/10.1007/s11042-021-10907-y. Epub ahead of print. PMID: 34155434
Ayan E, Ünver HM (2019) Diagnosis of pneumonia from chest X-ray images using deep learning. In: 2019 scientific meeting on electrical-electronics biomedical engineering and computer science (EBBT)
Bharati S et al (2020) Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlock 20(20):100391
Chowdhury MEH et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287
Ciano G, Andreini P, Mazzierli T, Bianchini M, Scarselli F (2021) A multi-stage GAN for multi-organ chest X-ray image generation and segmentation. https://arxiv.org/abs/2106.05132
Gonog L, Zhou Y (2019) A review: generative adversarial networks. In: 2019 14th IEEE conference on industrial electronics and applications (ICIEA), pp 505–510. https://doi.org/10.1109/ICIEA.2019.8833686
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. https://arxiv.org/abs/1406.2661
Hammoudi K, Benhabiles H, Melkemi M et al (2021) Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. J Med Syst 45:75. https://doi.org/10.1007/s10916-021-01745-4
Ibrahim AU, Ozsoz M, Serte S et al (2021) Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cogn Comput. https://doi.org/10.1007/s12559-020-09787-5
Kermany DS et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172:1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010
Khan W, Zaki N, Ali L (2021) Intelligent pneumonia identification from chest X-rays: a systematic literature review. IEEE Access 9:51747–51771. https://doi.org/10.1109/ACCESS.2021.3069937
Militante SV, Sibbaluca BG (2020) Pneumonia detection using convolutional neural networks. Int J Sci Technol Res 9(04). ISSN: 2277-8616
Mishra M, Parashar V, Shimpi R (2020) Development and evaluation of an AI system for early detection of Covid-19 pneumonia using X-ray (student consortium). In: 2020 IEEE sixth international conference on multimedia big data (BigMM)
Rajasenbagam T, Jeyanthi S, Pandian JA (2021) Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03075-2
Raji P, Prashantha HS (2021) Review on pneumonia detection using chest X rays. JCR 8(2):1801–1810. https://doi.org/10.31838/jcr.08.02.182
Rajpurkar P et al (2017) CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225
Salehinejad H, Colak E, Dowdell T, Barfett J, Valaee S (2019) Synthesizing chest X-ray pathology for training deep convolutional neural networks. IEEE Trans Med Imaging 38(5):1197–1206. https://doi.org/10.1109/TMI.2018.2881415
Shibly KH, Dey SK, Islam MT, Rahman MM (2020) COVID faster R-CNN: a novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images. Inform Med Unlock 20:100405. ISSN: 2352-9148
Verma G, Prakash S (2020) Pneumonia classification using deep learning in healthcare. Int J Innov Technol Expl Eng (IJITEE) 9(4). ISSN: 2278-3075
Victor Ikechukwu A, Murali S, Deepu R, Shivamurthy RC (2021) ResNet-50 vs VGG-19 vs training from scratch: a comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transit Proc 2(2):375–381. https://doi.org/10.1016/j.gltp.2021.08.027
Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10:19549. https://doi.org/10.1038/s41598-020-76550-z
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Chaithra, K.N., Shetty, S.P., Raji, P., Datta, A., Sandeep, K.S., Targolli, A. (2024). Detection of Pneumonia from Chest X-ray Using Deep Learning. In: Shetty, N.R., Prasad, N.H., Nagaraj, H.C. (eds) Advances in Communication and Applications . ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1105. Springer, Singapore. https://doi.org/10.1007/978-981-99-7633-1_31
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