Enhanced COVID-19 pneumonia diagnosis through optimized transformers and chest X-rays: post-pandemic insights
DOI:
https://doi.org/10.26507/paper.3216Palabras clave:
COVID-19, SARS-CoV-2, Medical image analysis, Transformer model, chest x-raysResumen
The COVID-19 pandemic led to an intense global health and economic impact, prompting researchers to seek rapid and effective diagnostic tools, such as Chest X-rays (CXR), especially in regions with limited resources. To alleviate the burden of manual CXR analysis, our study focuses on developing an optimized deep learning model to discern COVID-19 infections from other respiratory illnesses. We introduced an advanced screening tool comprising a Convolutional Neural Network (CNN) equipped with an optimized attention module. The training and validation of the model used a comprehensive private dataset of 5572 CXR images, illustrating various COVID-19 pneumonia severities, other pneumonia types, and healthy cases. Performance metrics showed training accuracy varying between 87% and 99.7%, with validation accuracy ranging from 87% to 93%. These results highlight the influence of dataset composition, especially the distribution of COVID-19 severity sub-classes, on the model's effectiveness. In the testing phase, the model performed optimally with a data set configuration rich in COVID-19 images, encompassing all sub-classes (sensitivity: 83%, false-negative rate: 17%). This study underscores the vital role of interdisciplinary cooperation in delivering expedient screening solutions during health crises. The proposed model achieved over 90% accuracy in identifying COVID-19 cases in CXRs, providing novel insights into the potential of imaging configurations for enhanced detection and differentiation of COVID-19 severities from other pneumonia instances.
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Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int J Infect Dis. 2020 Mar;92:214-7. https://doi.org/10.1016/j.ijid.2020.01.050
Cao K, Deng T, Zhang C, Lu L, Li L. A CNN-transformer fusion network for COVID-19 CXR image classification. PLoS One. 2022;17(10):e0276758. https://doi.org/10.1371/journal.pone.0276758
Bonet-Morón J, Ricciulli-Marín D, Pérez-Valbuena GJ, Galvis-Aponte LA, Haddad EA, Araújo IF, et al. Regional economic impact of COVID-19 in Colombia: An input-output approach. Regional Science Policy & Practice. 2020;12(6):1123-50. https://doi.org/10.1111/rsp3.12320
WHO Coronavirus (COVID-19) Dashboard [Internet]. [cited 2022 Nov 18]. Available from: https://covid19.who.int
Coronavirus Colombia [Internet]. [cited 2022 Nov 21]. Available from: https://www.ins.gov.co/Noticias/paginas/coronavirus.aspx
Vecino-Ortiz AI, Congote JV, Bedoya SZ, Cucunuba ZM. Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia. PLOS ONE. 2021 Mar 4;16(3):e0246987. https://doi.org/10.1371/journal.pone.0246987
Fisher D, Wilder-Smith A. The global community needs to swiftly ramp up the response to contain COVID-19. Lancet. 2020 Apr 4;395(10230):1109-10. https://doi.org/10.1016/S0140-6736(20)30679-6
Sánchez-Duque JA, Arce-Villalobos LR, Rodríguez-Morales AJ. Enfermedad por coronavirus 2019 (COVID-19) en América Latina: papel de la atención primaria en la preparación y respuesta. Aten Primaria. 2020;52(6):369-72. https://doi.org/10.1016/j.aprim.2020.04.001
Cascella M, Rajnik M, Aleem A, Dulebohn SC, Di Napoli R. Features, Evaluation, and Treatment of Coronavirus (COVID-19). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 [cited 2022 Nov 21]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK554776/
Clinical Spectrum [Internet]. COVID-19 Treatment Guidelines. [cited 2022 Nov 22]. Available from: https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/
Wong HYF, Lam HYS, Fong AHT, Leung ST, Chin TWY, Lo CSY, et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology. 2020 Aug;296(2):E72-8. https://doi.org/10.1148/radiol.2020201160
Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, et al. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic. Chest. 2020 Jul;158(1):106-16. https://doi.org/10.1016/j.chest.2020.04.003
Stephanie S, Shum T, Cleveland H, Challa SR, Herring A, Jacobson FL, et al. Determinants of Chest X-Ray Sensitivity for COVID- 19: A Multi-Institutional Study in the United States. Radiol Cardiothorac Imaging. 2020 Oct;2(5):e200337. https://doi.org/10.1148/ryct.2020200337
Gupta V, Jain N, Sachdeva J, Gupta M, Mohan S, Bajuri MY, et al. Improved COVID-19 detection with chest x-ray images using deep learning. Multimed Tools Appl. 2022;81(26):37657-80. https://doi.org/10.1007/s11042-022-13509-4
Parvaiz A, Khalid MA, Zafar R, Ameer H, Ali M, Fraz MM. Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection [Internet]. arXiv; 2022 [cited 2023 May 2]. Available from: http://arxiv.org/abs/2203.15269. https://doi.org/10.1016/j.engappai.2023.106126
He JL, Luo L, Luo ZD, Lyu JX, Ng MY, Shen XP, et al. Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China. Respiratory Medicine. 2020 Jul;168:105980. https://doi.org/10.1016/j.rmed.2020.105980
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. 2017 [cited 2023 May 2]; Available from: https://arxiv.org/abs/1706.03762
Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. Complex Intell Syst. 2021 Oct 1;7(5):2655-78. https://doi.org/10.1007/s40747-021-00424-8
Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020 Jun 1;43(2):635-40. https://doi.org/10.1007/s13246-020-00865-4
Khan MA, Azhar M, Ibrar K, Alqahtani A, Alsubai S, Binbusayyis A, et al. COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence. Computational Intelligence and Neuroscience. 2022 Jul 14;2022:e4254631. https://doi.org/10.1155/2022/4254631
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine. 2020 Jun 1;121:103792. https://doi.org/10.1016/j.compbiomed.2020.103792
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Derechos de autor 2023 Asociación Colombiana de Facultades de Ingeniería - ACOFI
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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