Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach
Abstract
:1. Introduction
- (1)
- We have proposed a composition of chest X-ray and symptoms data to detect COVID-19 status.
- (2)
- A formulaic solution has been proposed in this regard.
- (3)
- We have proposed the SymptomNet algorithm.
2. Materials and Methods
2.1. Proposed CNN Model
2.2. Symptom Analysis Using the Proposed SymptomNet Algorithm
2.3. Combining the CNN and SymptomNet Algorithms
2.4. Experiments
2.4.1. Data Preprocessing and Augmentation
2.4.2. Applied Proposed CNN Model
2.4.3. Applied Proposed SymptomNet Algorithm
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Optimizer Algorithm | Optimization Process |
---|---|
First-Order Optimization | These algorithms minimize or maximize a loss function E(x) by using its gradient values. Gradient descent is the most widely used first-order optimization algorithm. From the first-order derivative, it can determine whether the function is increasing or decreasing at a particular point. It gives us a tangential line to a point on its error surface. |
Second-Order Optimization | The second-order derivative, also known as the Hessian, minimizes or maximizes the Loss function. It uses the second-order partial derivatives matrix. The second order is not used much because it is costly to compute. The second-order derivative represents the function’s curvature by determining whether the first derivative is increasing or decreasing. The second-order derivative provides a quadratic surface. This quadratic surface touches the curvature of the error surface. |
Stochastic gradient descent | Stochastic gradient descent conducts parameter updating for each training example. This technique is usually faster. Stochastic gradient descent performs by updating the parameters one at a time. where are the training examples. |
Adagrad Optimizer | Approach of the AdaGrad optimizer is to use a different learning rate for each and every parameter θ at a time step based on the previous gradients that were calculated for that parameter. “It modifies the approach of general learning rate η at each time step t for every parameter θ based on the previous gradients that i have been computed for θi” [39]. |
AdaDelta | AdaDelta tends to eliminate the decaying learning rate problem of AdaGrad. Basically, it is an extension of AdaGrad. “Adadelta limits the window of accumulated past gradients to some fixed size w, Instead of accumulating all previous squared gradients” [39]. to a similar value as the momentum term, around 0.9. |
Adam | Adaptive Moment Estimation (Adam) is another technique that computes adaptive learning rates for each parameter. Like AdaDelta technique, in addition to storing the exponentially decay normal of previous squared Gradients, Adam likewise keeps an exponentially decaying normal of previous gradients . ⋯⋯ These are the formulas for the first moment (mean) and the second moment (the variance) of the gradients. The final formula for the parameter update is— |
References
- Bazell, R. How Genetic Mutations Turned the Coronavirus Deadly. Available online: http://nautil.us/issue/83/intelligence/how-genetic-mutations-turned-thecoronavirus-deadly?fbclid=IwAR3oUg2cDDqCGz4SmZVduxtCxUaPeBejnyUPkJtg34wrQTFec−OBzBz2×4 (accessed on 28 April 2020).
- World Health Organization (WHO). WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19. Available online: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-COVID-19---16-march-2020 (accessed on 16 March 2020).
- Sudre, C.H.; Lee, K.; Ni Lochlainn, M.; Varsavsky, T.; Murray, B.; Graham, M.S.; Menni, C.; Modat, M.; Bowyer, R.C.E.; Nguyen, L.H.; et al. Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app. medRxiv 2020. [Google Scholar] [CrossRef]
- Dong, E.; Du, H.; Gardener, L. An interactive web-based dashboard to track COVID-19 in real-time. Lancet Infect. Dis. 2020, 20, 533–534. [Google Scholar] [CrossRef]
- World Health Organization. Coronavirus Disease 2019 (COVID-19). Situation Report-72. 1 April 2020. Available online: https://apps.who.int/iris/bitstream/handle/10665/331685/nCoVsitrep01Apr2020-eng.pdf (accessed on 16 March 2020).
- Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir. Med. 2020, 8, 420–422. [Google Scholar] [CrossRef]
- Wu, Z.; McGoogan, J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in china: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA 2020, 323, 1239–1242. [Google Scholar] [CrossRef]
- Chen, H.; Guo, J.; Wang, C.; Luo, F.; Yu, X.; Zhang, W.; Li, J.; Zhao, D.; Xu, D.; Gong, Q. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records. Lancet 2020, 395, 420–422. [Google Scholar] [CrossRef] [Green Version]
- Rao, A.S.S.; Vazquez, J.A. Infection Control & Hospital Epidemiology. Identification of COVID-19 Can Be Quicker through Artificial Intelligence Framework Using a Mobile Phone-Based Survey in the Populations When Cities/Towns Are under Quarantine; Cambridge University Press: Cambridge, UK, 2020; pp. 1–18. [Google Scholar] [CrossRef] [Green Version]
- Fang, Y.; Zhang, H.; Xie, J.; Lin, M.; Ying, L.; Pang, P.; Ji, W. Sensitivity of chest ct for COVID-19: Comparison to rt-pcr. Radiology 2020, 296, 200432. [Google Scholar] [CrossRef]
- Liu, Y.; Gayle, A.A.; Wilder-Smith, A.; Rocklöv, J. The reproductive number of COVID-19 is higher compared to sars coronavirus. J. Travel Med. 2020, 27, taaa021. [Google Scholar] [CrossRef] [Green Version]
- Pandey, R.; Gautam, V.; Bhagat, K.; Sethi, T. A machine learning application for raising wash awareness in the times of COVID-19 pandemic. arXiv 2020, arXiv:2003.07074. [Google Scholar] [CrossRef]
- World Health Organization (WHO). Available online: https://www.who.int/ (accessed on 28 April 2020).
- Pan, F.; Ye, T.; Sun, P.; Gui, S.; Liang, B.; Li, L.; Zheng, D.; Wang, J.; Hesketh, R.L.; Yang, L.; et al. Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology 2020, 200370. [Google Scholar] [CrossRef]
- Aledhari, M.; Joji, S.; Hefeida, M.; Saeed, F. Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs. In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 18–21 November 2019; pp. 2405–2412. [Google Scholar] [CrossRef]
- Chouhan, V.; Singh, S.K.; Khamparia, A.; Gupta, D.; Tiwari, P.; Moreira, C.; Damaševičius, R.; De Albuquerque, V.H.C. A novel transfer learning-based approach for pneumonia detection in chest X-ray images. Appl. Sci. 2020, 10, 559. [Google Scholar] [CrossRef] [Green Version]
- Toğaçar, M.; Ergen, B.; Cömert, Z.; Özyurt, F. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM 2020, 41, 212–222. [Google Scholar] [CrossRef]
- Rajpurkar, P.; Irvin, J.; Zhu, K.; Yang, B.; Mehta, H.; Duan, T.; Ding, D.; Bagul, A.; Langlotz, C.; Shpanskaya, K. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv 2017, arXiv:1711.05225. [Google Scholar]
- Pankratz, D.G.; Choi, Y.; Imtiaz, U.; Fedorowicz, G.M.; Anderson, J.D.; Colby, T.V.; Myers, J.L.; Lynch, D.A.; Brown, K.K.; Flaherty, K.R. Usual interstitial pneumonia can be detected in transbronchial biopsies using machine learning. Ann. Am. Thorac. Soc. 2017, 14, 1646–1654. [Google Scholar] [CrossRef] [PubMed]
- Lakhani, P.; Sundaram, B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017, 284, 574–582. [Google Scholar] [CrossRef] [PubMed]
- Stephen, O.; Sain, M.; Maduh, U.J.; Jeong, D.-U. An efficient deep learning approach to pneumonia classification in healthcare. J. Healthc. Eng. 2019, 2019, 4180949. [Google Scholar] [CrossRef] [Green Version]
- Shin, H.-C.; Lu, L.; Kim, L.; Seff, A.; Yao, J.; Summers, R.M. Interleaved Text/image deep mining on a very large-scale radiology database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1090–1099. [Google Scholar] [CrossRef] [Green Version]
- Wang, X.; Peng, Y.; Lu, L.; Lu, Z.; Bagheri, M.; Summers, R.M. Chestxray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 2097–2106. [Google Scholar] [CrossRef] [Green Version]
- Zhao, X.; Zhang, B.; Li, P.; Ma, C.; Gu, J.; Hou, P.; Guo, Z.; Wu, H.; Bai, Y. Incidence, clinical characteristics, and prognostic factor of patients with COVID-19: A systematic review and meta- analysis. medRxiv 2020. [Google Scholar] [CrossRef]
- Chollet, F. Keras. 2015. Available online: https://github.com/fchollet/keras (accessed on 16 March 2020).
- Mooney, P. Chest X-ray Images (Pneumonia). Available online: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia (accessed on 28 April 2020).
- Keras, Image Preprocessing. Available online: https://keras.io/preprocessing/image/ (accessed on 28 April 2020).
- Chowdhury, I.; Alo, P. Available online: https://www.prothomalo.com/northamerica/article/1653129/ (accessed on 28 April 2020).
- Hossain, M.N.; Uddin, M.H.; Thapa, K.; Zubaer, M.A.M.; Islam, M.S.; Lee, J.; Park, J.; Yang, S.-H. Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach. J. Healthc. Eng. 2021. [Google Scholar] [CrossRef]
- Government of Canada, Symptoms of COVID-19. Available online: https://www.canada.ca/en/publichealth/services/diseases/2019-novel-coronavirusinfection/symptoms.htmls (accessed on 28 April 2020).
- Government of United Kingdom, Coronavirus (COVID-19). Available online: https://www.nhs.uk/conditions/coronaviruscovid-19/ (accessed on 28 April 2020).
- Department of Health, Australian Government. Coronavirus (COVID-19) Health Alert. Available online: https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert (accessed on 28 April 2020).
- Centers of Disease Control and Prevention, Symptoms of Coronavirus. Available online: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html (accessed on 28 April 2020).
- Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Rajendra Acharya, U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. J. Comput. Biol. Med. 2020, 121, 103792. [Google Scholar] [CrossRef]
- Asif, S.; Wenhui, Y.; Jin, H.; Tao, Y.; Jinhai, S. Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks. medRxiv 2020. [Google Scholar] [CrossRef]
- Purohit, K.; Kesarwani, A.; Kisku, D.R.; Dalui, M. COVID-19 Detection on Chest X-ray and CT Scan Images Using Multi-image Augmented Deep Learning Model. BioRxiv 2020, 205567. [Google Scholar] [CrossRef]
- McAllister, D.A.; Liu, L.; Shi, T.; Chu, Y.; Reed, C.; Burrows, J.; Adeloye, D.; Rudan, I.; Black, R.E.; Campbell, H.; et al. Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: A systematic analysis. Lance Glob. Health 2019, 7, e47–e57. [Google Scholar] [CrossRef] [Green Version]
- Uddin, M.H.; Ara, J.M.K.; Rahman, M.H.; Yang, S.H. Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition. In Proceedings of the 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh, 14–16 September 2021; pp. 1–4. [Google Scholar] [CrossRef]
- Ruder, S. An overview of gradient descent optimization algorithms. arXiv 2017, arXiv:1609.04747. [Google Scholar]
Clinical Symptoms | Symptom Weights (%) |
---|---|
Fever | 88.4 |
Cough | 71.1 |
Fatigue | 60.3 |
Shortness of breath | 44.2 |
Muscle pain | 26 |
Chill | 26 |
Dizziness | 16.1 |
Headache | 11.3 |
Sore throat | 7.8 |
Nausea or vomiting | 5.9 |
Diarrhea | 5.7 |
Nasal congestion | 2.8 |
CNN Classifier | SymptomNet Algorithm | COVID-19 Prediction |
---|---|---|
Pneumonia | ≥Threshold | High Possibility |
Pneumonia | <Threshold | Moderate Possibility |
Normal | Threshold < Cpr ≤ 70 | Low possibility |
Normal | Cpr > 70 | High Possibility |
Normal | <Threshold | No Infection |
Method | Setting |
---|---|
Rotation range | 40 |
Width shift | 0.2 |
Rescale | 1.0/255 |
Height shift | 0.2 |
Zoom range | 0.3 |
Horizontal flip | True |
Patients | Symptoms | COVID-19 Status | State, Country | Gender |
---|---|---|---|---|
Patient 1 | Fever, shortness of breath, cough, sore throat, muscle pain, and hyposmia (having difficulties smelling food and other things) | Positive | Dhaka, Bangladesh | Female |
Patient 2 | Fever, sore throat, shortness of breath, body chills, cough, muscle pain, and cannot eat food | Positive | Dhaka, Bangladesh | Female |
Patient 3 | Fever, shortness of breath, cough, muscle pain, and kidney problems | Positive | New York, NY, USA (Bangladeshi Immigrant) | Male |
Patient 4 | Fever, cough, sore throat, and muscle pain | Positive | New York, NY, USA (Bangladeshi Immigrant) | Male |
Patient 5 | Muscle pain, fatigue (tiredness), fever, and cough | Positive | Gaibandha, Bangladesh | Male |
Clinical Symptoms | Individual Weight Iw = (w × 100)/Total Weight | Weight Summation/Prediction Line ws = Iw + wsprevious |
---|---|---|
Fever | 24.2 | 24.32 |
Cough | 19.45 | 43.76 |
Fatigue | 16.49 | 60.25 |
Shortness of breath | 12.1 | 72.34 |
Muscle pain | 7.11 | 79.45 |
Chill | 7.11 | 86.56 |
Dizziness | 4.4 | 90.96 |
Headache | 3.09 | 94.05 |
Sore throat | 2.13 | 96.18 |
Nausea or vomiting | 1.61 | 97.79 |
Diarrhea | 1.55 | 99.34 |
Nasal congestion | 0.76 | 100 |
Clinical Symptoms | Symptom Weights |
---|---|
Fever | 92 |
Cough | 86 |
body chills | 78 |
Shortness of breath | 74 |
Muscle pain | 58 |
Sore throat | 52 |
Fatigue | 2 |
Hyposmia (having difficulties smelling food and other things) | 2 |
Cannot eat food | 2 |
Kidney problem | 2 |
Total Weight | 448 |
Clinical Symptoms |
Individual Weight Iw = (w × 100)/Total Weight | Weight Summation/Prediction Line ws = Iw + wsprevious |
---|---|---|
Fever | 20.54 | 20.54 |
Cough | 19.19 | 39.74 |
body chills | 17.41 | 57.15 (Threshold point) |
Shortness of breath | 16.52 | 73.66 |
Muscle pain | 12.95 | 86.62 |
Sore throat | 11.61 | 98.23 |
Fatigue | 0.045 | 98.67 |
Hyposmia (having difficulties smelling food and other things) | 0.045 | 98.72 |
Cannot eat food | 0.045 | 98.77 |
Kidney problem | 0.045 | 100 |
Patient | Threshold | Original COVID-19 Status | Prediction Accuracy | |
---|---|---|---|---|
patient 1 | 57.15 | 80.055 (positive) | positive | Correct |
patient 2 | 57.15 | 98.265 (positive) | positive | Correct |
patient 3 | 57.15 | 69.245 (positive) | positive | Correct |
patient 4 | 57.15 | 64.29 (positive) | positive | Correct |
patient 5 | 57.15 | 57.725 (positive) | positive | Correct |
patient 6 | 57.15 | 70.22 (positive) | positive | Correct |
patient 7 | 57.15 | 86.61 (positive) | positive | Correct |
patient 8 | 57.15 | 86.61 (positive) | positive | Correct |
patient 9 | 57.15 | 86.61 (positive) | positive | Correct |
patient 10 | 57.15 | 98.265 (positive) | positive | Correct |
patient 11 | 57.15 | 54.57 (negative) | positive | Wrong |
Patient No. | CNN Classified as | CNN Accuracy | SymptomNet Threshold | SymptomNet PREDICTION (%) | SymptomNet Prediction Accuracy | Our Model’s Prediction | Patient’s Original COVID-19 Status | Model Prediction Accuracy Status |
---|---|---|---|---|---|---|---|---|
1 | pneumonia | 98.21 (positive) | High possibility | Positive | Correct | |||
2 | pneumonia | 74.1 (positive) | High possibility | Positive | Correct | |||
3 | pneumonia | 98.21 (positive) | High possibility | Positive | Correct | |||
4 | pneumonia | 98.21 (positive) | High possibility | Positive | Correct | |||
5 | normal | 95.9% | 57.15 | 57.59 (positive) | 97% | Low possibility | Positive | Correct |
6 | pneumonia | 85.26 (positive) | High possibility | Positive | Correct | |||
7 | normal | 36.6 (negative) | No Infection | Positive | Wrong | |||
8 | pneumonia | 98.21 (positive) | High possibility | Positive | Correct | |||
9 | normal | 57.15 (positive) | Low possibility | Positive | Correct | |||
10 | normal | 74.1 (positive) | High possibility | Positive | Correct | |||
11 | pneumonia | 83.56 (positive) | High possibility | Positive | Correct | |||
12 | normal | 33.26 (negative) | No Infection | Negative | Correct | |||
13 | pneumonia | 96.32 (positive) | High possibility | Positive | Correct | |||
14 | pneumonia | 72.31 (positive) | High possibility | Positive | Correct | |||
15 | pneumonia | 95.71 (positive) | High possibility | Positive | Correct | |||
16 | pneumonia | 93.32 (positive) | High possibility | Positive | Correct | |||
17 | normal | 36.6 (negative) | No Infection | Positive | Wrong | |||
18 | pneumonia | 96.27 (positive) | High possibility | Positive | Correct | |||
19 | normal | 59.75 (positive) | Low possibility | Positive | Correct | |||
20 | normal | 84.29 (positive) | High possibility | Positive | Correct | |||
21 | pneumonia | 91.36 (positive) | High possibility | Positive | Correct | |||
22 | normal | 35.36 (negative) | No Infection | Negative | Correct | |||
23 | pneumonia | 66.32 (positive) | Low possibility | Negative | Wrong | |||
24 | pneumonia | 82.71 (positive) | High possibility | Positive | Correct | |||
25 | pneumonia | 97.51 (positive) | High possibility | Positive | Correct | |||
26 | pneumonia | 96.62 (positive) | High possibility | Positive | Correct | |||
27 | normal | 66.6 (positive) | Low possibility | Positive | Correct | |||
28 | pneumonia | 86.37 (positive) | High possibility | Positive | Correct | |||
29 | normal | 58.85 (positive) | Low possibility | Positive | Correct | |||
30 | normal | 84.29 (positive) | High possibility | Positive | Correct |
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Helal Uddin, M.; Hossain, M.N.; Islam, M.S.; Zubaer, M.A.A.; Yang, S.-H. Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach. COVID 2022, 2, 117-137. https://doi.org/10.3390/covid2020009
Helal Uddin M, Hossain MN, Islam MS, Zubaer MAA, Yang S-H. Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach. COVID. 2022; 2(2):117-137. https://doi.org/10.3390/covid2020009
Chicago/Turabian StyleHelal Uddin, Mohammad, Mohammad Nahid Hossain, Md Shafiqul Islam, Md Abdullah Al Zubaer, and Sung-Hyun Yang. 2022. "Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach" COVID 2, no. 2: 117-137. https://doi.org/10.3390/covid2020009