Kelding Jahemar Jacho
ABSTRACT
The early detection of COVID-19 is one of the current challenges in developing effective diagnosis and treatment mechanisms for patients who are at a high risk for community contagion. Computed Tomography (CT) is an essential support for detecting the infection pattern that causes this disease. CT scans provide relevant information on the morphological appearance of the infected parenchymal tissue, known as ground-glass opacities. Artificial Intelligence (AI) can assist in the quick evaluation of CT scans to differentiate COVID-19 findings in suggestive clinical cases. In this context, AI in the form of, Convolutional Neural Networks (CNN), has achieved successful results in the analysis and classification of medical images. A deep CNN architecture is proposed in this study to diagnose COVID-19 based on the classification of Chest Computed Tomography (CCT) images. In this study 8,624 CCTs of Ecuadorian patients affected by COVID-19 in the first quarter of 2021, were examined. The initial review of CCTs was performed by medical experts to discriminate the CCTs against other chronic lung diseases not associated with COVID-19. The CCTs were pre-processed by techniques such as morphological segmentation, erosion, dilation, and adjustment. After training the model reached an overall F1-score of 97%.
Supplemental Material
Available for Download
Presentation slides
- [n. d.]. Segmentación activa del contorno 3D guiada por el usuario de Estructuras anatómicas: eficiencia y confiabilidad significativamente mejoradas. Neuroimage ([n. d.]).Google Scholar
- Michael Chung, Adam Bernheim, Xueyan Mei, Ning Zhang, Mingqian Huang, Xianjun Zeng, Jiufa Cui, Wenjian Xu, Yang Yang, Zahi A Fayad, Adam Jacobi, Kunwei Li, Shaolin Li, and Hong Shan. 2020. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 295, 1 (2020), 202–207. https://doi.org/10.1148/radiol.2020200230Google ScholarCross Ref
- Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, and Timothy L Kline. 2017. Machine Learning for Medical Imaging. RadioGraphics 37, 2 (2017), 505–515. https://doi.org/10.1148/rg.2017160130Google ScholarCross Ref
- Fangfang Fu, Jianghua Lou, Deyan Xi, Yan Bai, Gongbao Ma, Bin Zhao, Dong Liu, Guofeng Bao, Zhidan Lei, and Meiyun Wang. 2020. Chest computed tomography findings of coronavirus disease 2019 (COVID-19) pneumonia. European Radiology 30, 10 (2020), 5489–5498. https://doi.org/10.1007/s00330-020-06920-8Google ScholarCross Ref
- Alexander Gepperth and Barbara Hammer. 2016. Incremental learning algorithms and applications. In European Symposium on Artificial Neural Networks (ESANN). Bruges, Belgium. https://hal.archives-ouvertes.fr/hal-01418129Google Scholar
- Parisa Gifani, Ahmad Shalbaf, and Majid Vafaeezadeh. 2021. Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans. International Journal of Computer Assisted Radiology and Surgery 16, 1(2021), 115–123. https://doi.org/10.1007/s11548-020-02286-wGoogle ScholarCross Ref
- Tripti Goel, R Murugan, Seyedali Mirjalili, and Deba Kumar Chakrabartty. 2021. OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence 51, 3 (2021), 1351–1366. https://doi.org/10.1007/s10489-020-01904-zGoogle ScholarDigital Library
- Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, Maxime Blain, Michael Kassin, Dilara Long, Nicole Varble, Stephanie M Walker, Ulas Bagci, Anna Maria Ierardi, Elvira Stellato, Guido Giovanni Plensich, Giuseppe Franceschelli, Cristiano Girlando, Giovanni Irmici, Dominic Labella, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Ronald M Summers, Peter L Choyke, Daguang Xu, Mona Flores, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Francesca Patella, Maurizio Cariati, Gianpaolo Carrafiello, Peng An, Bradford J Wood, and Baris Turkbey. 2020. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications 11, 1 (2020), 4080. https://doi.org/10.1038/s41467-020-17971-2Google ScholarCross Ref
- Chaolin Huang, Yeming Wang, Xingwang Li, Lili Ren, Jianping Zhao, Yi Hu, Li Zhang, Guohui Fan, Jiuyang Xu, Xiaoying Gu, Zhenshun Cheng, Ting Yu, Jiaan Xia, Yuan Wei, Wenjuan Wu, Xuelei Xie, Wen Yin, Hui Li, Min Liu, Yan Xiao, Hong Gao, Li Guo, Jungang Xie, Guangfa Wang, Rongmeng Jiang, Zhancheng Gao, Qi Jin, Jianwei Wang, and Bin Cao. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395, 10223 (2020), 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5Google ScholarCross Ref
- Gerardo Patricio Inca Ruiz and Ana Cristina Inca León. 2020. Evolución de la enfermedad por coronavirus (COVID‐19) en Ecuador. La Ciencia al Servicio de la Salud y la Nutrición 11, 1(2020), 5–15. https://doi.org/10.47244/cssn.Vol11.Iss1.441Google Scholar
- Cheng Jin, Weixiang Chen, Yukun Cao, Zhanwei Xu, Zimeng Tan, Xin Zhang, Lei Deng, Chuansheng Zheng, Jie Zhou, Heshui Shi, and Jianjiang Feng. 2020. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature Communications 11, 1 (2020), 5088. https://doi.org/10.1038/s41467-020-18685-1Google ScholarCross Ref
- Zachary Chase Lipton, Charles Elkan, and Balakrishnan Narayanaswamy. 2014. Thresholding classifiers to maximize f1 score. ArXiv (2014), 1402–1892.Google Scholar
- Eri Matsuyama. 2020. A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images. Journal of Biomedical Science and Engineering 13, 07(2020), 140–152. https://doi.org/10.4236/jbise.2020.137014Google ScholarCross Ref
- Pravin Pokhrel, Changpeng Hu, and Hanbin Mao. 2020. Detecting the Coronavirus (COVID-19). ACS Sensors 5, 8 (2020), 2283–2296. https://doi.org/10.1021/acssensors.0c01153Google ScholarCross Ref
- Matteo Polsinelli, Luigi Cinque, and Giuseppe Placidi. 2020. A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognition Letters 140 (2020), 95–100. https://doi.org/10.1016/j.patrec.2020.10.001Google ScholarDigital Library
- Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, and Gyu Sang Choi. 2021. An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification. Complexity 2021(2021), 6621607. https://doi.org/10.1155/2021/6621607Google ScholarCross Ref
- U Ruby and V Yendapalli. 2020. Binary cross entropy with deep learning technique for image classification. International Journal of Advanced Trends in Computer Science and Engineering 9, 10(2020).Google Scholar
- Vruddhi Shah, Rinkal Keniya, Akanksha Shridharani, Manav Punjabi, Jainam Shah, and Ninad Mehendale. 2021. Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emergency Radiology (2021). https://doi.org/10.1007/s10140-020-01886-yGoogle Scholar
- Woldometer. 2020. Covid-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/#countriesGoogle Scholar
- Samir S Yadav and Shivajirao M Jadhav. 2019. Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data 6, 1 (2019), 113. https://doi.org/10.1186/s40537-019-0276-2Google ScholarCross Ref
- Qingsen Yan, Bo Wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Qinfeng Shi, Shuo Jin, Liang Zhang, and Zheng You. 2020. COVID-19 Chest CT Image Segmentation – A Deep Convolutional Neural Network Solution. (2020). arxiv:2004.10987http://arxiv.org/abs/2004.10987Google Scholar
- Chuansheng Zheng, Xianbo Deng, Qiang Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, and Xinggang Wang. 2020. Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label. medRxiv (jan 2020), 2020.03.12.20027185. https://doi.org/10.1101/2020.03.12.20027185Google Scholar
Recommendations
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
AbstractComputer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) ...
Highlights- The effects of various CXR enhancement techniques were extensively studied on plain and segmented CXR image classification.
COVID-19 infection localization and severity grading from chest X-ray images
AbstractThe immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has ...
Highlights- Localization, Quantification, and Severity Grading of COVID-19 from Chest X-rays.
P2P-COVID-GAN: Classification and Segmentation of COVID-19 Lung Infections From CT Images Using GAN
Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due ...
Comments