Abstract
Coronavirus 2019, well familiar as COVID-19, is a virus that causes significant pneumonia and has varying degrees of severity based on the patient’s capability. The coronavirus infection was initially discovered in the Chinese town of Wuhan in Dec. 2019 and quickly spread around the world as a worldwide pandemic. Early detection of positive cases and prompt treatment of infected individuals is required to prevent viral transmission. The necessity of testing kits for COVID-19 has grown, and most of the growing nations are encountering a scarcity of testing kits as new cases emerge daily. In this case, the current study is with the help of radiology imaging techniques, including X-ray, to help in detecting COVID-19. In several disease diagnoses and decision-making circumstances, the information provided in a chest X-ray sample is sufficient to assist medical experts. With the help of a Deep Convolutional Neural Network (CNN), the research proposes an intelligent method to classify various nine diseases, including coronavirus disease 2019, with the help of X-ray instances applying pre-trained DenseNet169 architecture. The fundamental goal of this paper is to classify lung diseases with COVID-19. The used datasets are collected from online repositories, i.e., Kaggle and NIH contained X-ray images of all nine classes. This dataset consists of 1200 images for each class. Various rotations and scaling operations have been applied to the dataset, and the data in the dataset are divided into the test, train, and validation sets. In comparison to other studies in the literature, our models performed well. The highest accuracy attained by DenseNet169 is for COVID-19 with an accuracy of 99.4%, F1-score of 97.5%, precision of 97%, recall of 98%, and specificity of 99.6%. The highest True Positive rate we got in this is 99% for COVID-19, followed by 97% for Cardiomegaly. The minimal rate we got is 88% in Atelectasis. DesnseNet169 proved to be more robust and reliable in classifying nine classes, including COVID-19, after adopting a testing strategy proposed in the literature, making them suitable methods for classification using chest X-ray samples. Which in the future will be helpful for radiologists and physicians during the pandemic of Coronavirus 2019.
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References
Karakanis S, Leontidis G (2020) Lightweight deep learning models for detecting COVID-19 from chest X-ray images. 12 December
Wang S, Kang B, Ma J, Zeng X, Xiao M (2021) A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). January 26
Bhosale YH, Patnaik KS (2022) IoT deployable lightweight deep learning application for COVID-19 detection with lung diseases using RaspberryPi. In: 2022 International Conference on IoT and Blockchain Technology (ICIBT), pp 1–6. https://doi.org/10.1109/ICIBT52874.2022.9807725
Bhosale YH (2020) Digitization of households with population using cluster and list sampling frame in aerial images 5(2):22–26 www.oaijse.com
Khan AA, Shafiq S, Kumar R, Kumar J, Haq AU (2021) H3dnn: 3d deep learning based detection of covid-19 virus using lungs computed tomography. February 24
Ouyang X et al (2020) Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 39(8):2595–2605
Kumar R et al (2020) Blockchain-federated-learning and deep learning models for COVID-19 detection using CT Imaging
Wang G et al (2020) A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT Images. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.3000314
Sedik A, Hammad M, Fathi E, El-Samie A, Gupta BB, Ahmed A, El-Latif A (2020) Efficient deep learning approach for augmented detection of coronavirus disease. 29 September
Asif S, Wenhui Y (2020) Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning. medRxiv
De Moura J, Novo J, Ortega M (2020) Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images. medRxiv
Rajaraman S, Antani SK (2020) Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection. medRxiv
Oh Y, Park S, Ye JC (2020) Deep learning COVID19 features on CXR using limited training data sets. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.2993291
Roy S et al (2020) Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.2994459
Zhou L et al (2020) A rapid, accurate and machine agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2020.3001810
Medifee. X-ray cost. Accessed 20 Dec 2021. Available on https://www.medifee.com/tests/x-ray-cost/
Govt slashes RT-PCR test, sample collection costs. Accessed 20 Dec 2021. Available on https://timesofindia.indiatimes.com/city/mangaluru/govt-slashes-rt-pcr-test-sample-collection-costs/articleshow/87303471.cms
Rajpurkar P et al. (2017) Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225
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
Wang L, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv preprint arXiv:2003.09871
Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from xray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 1
Narin A, Kaya C, Pamuk Z 2020 Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849
Sethy K, Behera SK (2020) Detection of coronavirus disease (covid-19) based on deep features. Preprints 2020030300:2020
Asif S, Wenhui Y, Jin H, Tao Y, Jinhai S (2020) Classification of COVID-19 from chest X-ray images using deep convolutional neural networks. medRxiv
Mangal A et al. (2020) CovidAID: COVID-19 detection using chest X-ray. arXiv preprint arXiv:2004.09803
Viral and bacterial pneumonia dataset. Accessed 25 Oct 2021. Available on https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
COVID-19 dataset. Accessed 25 Oct 2021. Available on https://www.kaggle.com/c/siim-covid19-detection
Lung diseases dataset. Accessed 25 Oct 2021. Available on https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/37178474737
Bhosale YH, Zanwar S, Ahmed Z, Nakrani M, Bhuyar D, Shinde U (2022) Deep convolutional neural network based Covid-19 classification from radiology x-ray images for IoT enabled devices. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 1398–1402. https://doi.org/10.1109/ICACCS54159.2022.9785113
Sharma S (2017) Activation functions in neural networks [Online]. Available: https://towardsdatascience.com/activation-functions-neuralnetworks-1cbd9f8d91d6
Nicholson C (2019) Evaluation metrics for machine learning - accuracy, precision, recall, and F1 defined [Online]. Available: https://pathmind.com/wiki/accuracy-precision-recall-f1
Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V (2020) Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet, Chaos, Solitons and Fractals 138:109944. https://doi.org/10.1016/j.chaos.2020.109944
Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S (2020) Deep transfer learning based classification model for COVID-19 disease, Irbm. https://doi.org/10.1016/j.irbm.2020.05.003
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Bhosale, Y.H., Singh, P., Patnaik, K.S. (2023). COVID-19 and Associated Lung Disease Classification Using Deep Learning. In: Gupta, D., Khanna, A., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 492. Springer, Singapore. https://doi.org/10.1007/978-981-19-3679-1_22
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