Abstract
Coronaviruses are a cluster of viruses belonging to the family of Coronaviridae, which infect animals and humans. Coronaviruses related to humans can cause mild disease very similar to common flu, which others cause more severe acute diseases. The new COVID-19, which was first detected in the city of Wuhan in Hubei province, China in December 2019. This new coronavirus that previously has not been identified emerged in China when a cluster of Pneumonia cases was reported. Signs and symptoms include respiratory symptoms namely, chronic mucus, fever, lingering chest pain, stubborn cough, breathing noisily. In more severe cases, the virus can lead to cause pneumonia, acute respiratory distress syndrome (ARDS), and sometimes death. The Health care system and the global economy has been severely disrupted since the Covid-19 pandemic began. An early diagnosis to identify the infection is very crucial to mitigate the stress on the health care system and health care providers. A chest X-ray is performed on patients to detect any inflammation in the lungs of a human. The objective of this paper is to leverage artificial intelligence models coupled with image augmentation techniques to accurately classify the chest X-ray images into two classes namely, Pneumonia and Normal. In this research, a new framework, CovidSORT, is proposed for detecting pneumonia infected lungs using chest X-ray images. The proposed framework is developed using deep transfer learning models namely, Inception-V3, VGG16, VGG19, ResNet-50, DenseNet-121 and MobileNetV2 which were pre-trained on ImageNet which led to quicker model training. Additionally, these models are fine-tuned with image augmentation techniques for better accuracy. The research concludes that the ensemble model built on the majority voting approach from these models in identifying pneumonia has achieved a classification accuracy of 96.83%. The above framework can be used by radiologists to corroborate and identify COVID patients quickly.
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References
Ahmad S, Hafeez A, Siddqui SA, Ahmad M, Mishra S (2020) A review of COVID-19 (coronavirus disease-2019) diagnosis, treatments and prevention. EJMO 4(2):116–125
Yuen K, Ye Z, Fung S et al (2020) SARS-CoV-2 and COVID-19: the most important research questions. Cell Biosci 10:40
Wu F, Zhao S, Yu B et al (2020) A new coronavirus associated with human respiratory disease in China. Nature 579(7798):265–269
Preston SR, Haines MR (1991) Fatal years—child mortality in late 19th century America. Princeton University Press, Princeton, pp 4–5
Rudan I, Tomaskovic L, Boschi-Pinto C, Campbell H (2004) Global estimate of the incidence of clinical pneumonia among children under five years of age. Bull World Health Organ 82:895–903
Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10:257–273
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR (2019) Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Image Graph 78:101673
Rajpurkar P, Irvin J et al (2017) Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning
Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recog Lett 133:232–239. ISSN 0167-8655
Esteva A, Kuprel B, Novoa RA et al. Dermatologist-level classification of skin cancer with deep neural networks
Gaál G, Maga B, Lukács A. Attention U-net based adversarial architectures for chest X-ray lung segmentation
Bobić V, Tadić P, Kvaščev G (2016) Hand gesture recognition using neural network based techniques. In: 2016 13th symposium on neural networks and applications (NEUREL). IEEE, pp 1–4
Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6:60
Francisco JM-B, Fiammetta S, Jose MJ, Daniel U, Leonardo F (2018) Forward noise adjustment scheme for data augmentation. arXiv preprints
Szegedy, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. 1512.00567, arXiv
Ganesh Samarth CA, Bhowmik N, Breckon TP (2019) Experimental exploration of compact convolutional neural network architectures for non-temporal real-time fire detection. 1911.09010
Simonyan K, Zisserman A (2015) Very deep neural networks for large-scale image recognition. In: International Conference on Learning Representations
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345–1359. https://doi.org/10.1109/TKDE.2009.191
Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest X-ray classification. In: Computer vision and pattern recognition. arXiv:1803.02315
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. 608.06993, arXiv
Howard A, Zhmoginov A, Chen L-C, Sandler M, Zhu M (2018) Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation
Petsiuk V, Das A, Saenko K (2018) RISE: randomized input sampling for explanation of black-box models. 1806.07421, arXiv
Mangal A, Kalia S, Rajgopal H, Rangarajan K, Namboodiri V, Banerjee S, Arora C (2020) CovidAID: COVID-19 detection using chest X-ray. 2004.09803
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Tammina, S. (2022). CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer Learning Models. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_37
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DOI: https://doi.org/10.1007/978-981-16-3690-5_37
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