Abstract
The Covid-19 spun into a pandemic and has affected routine lives and global health. It is crucial to identify the infectious Covid-19 subjects as early as possible to avert its spread. The CXR images processed with deep learning (DL) processes have newly become an earnest method for early Covid-19 detection along with the regular RT-PCR test. This paper examines the deep learning (DL) models to detect Covid-19 from CXR images for early analysis of Covid-19. We conducted an empirical study to assess the efficacy of the proposed convolutional neural network DL model (CNN-DLM), pre-trained with some eminent networks such as MobileNet, InceptionNet-V3, ResNet50, Xception, and DenseNet121 for initial detection of Covid-19 for an openly accessible dataset. We also exhibited the accuracy and loss value curves for the selected number of epochs for all these models. The results indicate that with the proposed CNN model pre-trained with the DenseNet121 greater results were achieved compared to other pre-trained CNN-DLMs applied in a transfer learning approach.
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References
World Health Organization (2020) Coronavirus disease 2019 (COVID-19) Situation Report–196 [cited 3 Aug 2020]
Rahmani AM et al (2022) Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-12952-7
Agrawal T, Choudhary P (2021) FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images. Evol Syst. https://doi.org/10.1007/s12530-021-09385-2
Durrani M, Haq I u, Kalsoom U, Yousaf A (2020) Chest X-rays findings in COVID 19 patients at a University Teaching Hospital—A descriptive study. Pakistan J Med Sci 36 (COVID19-S4):S22
Sakib S et al (2022) Detection of COVID-19 disease from chest X-ray images: a deep transfer learning framework. MedRxiv. https://doi.org/10.1101/2020.11.08.20227819
Nyemeesha V, Ismail BM (2021) Method to enhance classification of skin cancer using back propagated artificial neural network applied intelligence and informatics. AII 2021. Commun Comput Inf Sci 1435
Ismail M et al (2019) An effective heart disease prediction method using artificial neural network. Int J Innov Technol Exploring Eng 8(8):1529–1532
Hemdan EED, Shouman MA, Karar ME (2020) COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-Ray Images, arXiv preprint arXiv: 2003.11055
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra AU (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640
Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 24(3):1207–1220
Muqeet MA, Quadri MU, Sasidhar K, Krishna PS (2022) Deep learning-based prediction of nCOVID-19 disease using chest X-ray images (CXRIs). In: Chaurasia MA, Mozar S (eds) Contactless healthcare facilitation and commodity delivery management during COVID 19 pandemic. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-16-5411-4_3
Ucar F, Korkmaz D (2020) COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140:109761
Farooq M, Hafeez A (2003) COVID-ResNet: a deep learning framework for screening of COVID19 from radiographs, 2020, arXiv preprint arXiv: 2003.14395
Wang A, Wong, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images, 2020, arXiv preprint arXiv:2003.09871
Sethy PK, Behera SK, Detection of coronavirus disease (COVID-19) based on deep features, 2020. https://doi.org/10.20944/preprints202003.0300.v1
Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. https://github.com/ieee8023/Covid-chestxray-dataset
Shi F et al (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. https://arxiv.org/abs/2004.0273
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp 4510–4520
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp 2818–2826
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, 2016, pp 770–778
Chollet F (2017) Xception: deep learning with depth wise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition 2017, pp 1800–1807
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, 21–26 July 2017, pp 4700–4708
Muqeet MA, Holambe RS (2018) A collaborative representation face classification on separable adaptive directional wavelet transform based completed local binary pattern features. Eng Sci Technol, Int J 21(4)
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Muqeet, M.A., Hameeduddin, Q.M., Mohammed Ismail, B., Mohammad, A.B., Qadeer, S., Muzammil Parvez, M. (2023). An Empirical Study of CNN-Deep Learning Models for Detection of Covid-19 Using Chest X-Ray Images. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_17
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