COVID-19 Suspects Monitoring System Based on Symptom recognition using Deep Neural Network
The outbreak of the Corona virus or COVID-19 was still a global concern even though it has been declared an endemic in several countries in the world, including Indonesia. However, with the emergence of new variants of this virus, preventive efforts continue to be made to prevent its spread. To prevent the spread of this virus, early detection was important, especially in knowing prospective clients who are positive and reactive to this virus, thus enabling early isolation measures for prospective patients who are taking action. This identification can be carried out in public areas that are the center of community activities. In this study, an intelligent system will be developed that can detect people suspected of COVID-19 through fever and breathing problem symptoms that can provide solutions to prevent the spread of this virus. Identify these symptoms through thermography-based image processing sourced from thermal camera sensors and then look for the possibility of suspected and reactive COVID19. Furthermore, the AI model was used by the early detection system of people suspected of being positive and reactive for COVID-19 using the Deep Neural Network method. This study aims to identify symptoms of fever and respiratory infection through image processing sourced from thermal camera sensors and further diagnose prospective patients who are suspected of being positive and reactive for COVID19 using the CNN method as an intelligent system for early detection of suspected positive and reactive COVID19 patientsIn the process of testing the classification training model, the performance results in the CNN classification process have an accuracy value of more than 88%. Furthermore, a comparison was made between the CNN classification and other classifications, such as SVM, Naive Bayes and Multi-Layer Perceptron (MLP). The results obtained from this comparison have an average percentage of accuracy above 80%. MLP has the lowest accuracy among its classification methods of 83.56%. CNN has the highest accuracy value compared to other methods of 88.68%. Therefore, CNN can be chosen to be the right one for use in the COVID-19 suspect detection system through the recognition of symptoms and respiratory disorders. Based on these performance measurements, the process of detecting COVID19 suspects indicated by health symptoms can be applied to real data.
 R. Boulding, R. Stacey, R. Niven, and S. J. Fowler, “Dysfunctional breathing: A review of the literature and proposal for classification,” European Respiratory Review, vol. 25, no. 141, pp. 287–294, 2016, doi: 10.1183/16000617.0088-2015.
 Z. Xu et al., “Pathological findings of COVID-19 associated with acute respiratory distress syndrome,” The Lancet Respiratory Medicine, vol. 8, no. 4, pp. 420–422, 2020, doi: 10.1016/S2213-2600(20)30076-X.
 W. Taylor et al., “A review of the state of the art in non-contact sensing for covid-19,” Sensors (Switzerland), vol. 20, no. 19, pp. 1–19, 2020, doi: 10.3390/s20195665.
 M. Hu, Y. Fan, and X. Chen, “Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement,” Journal of Biomedical Optics, vol. 22, no. 3, pp. 1–11, 2022, doi: 10.1117/1.JBO.22.3.036006.
 L. Li et al., “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy,” Radiology, vol. 296, no. 2, pp. E65–E71, 2020, doi: 10.1148/radiol.2020200905.
 T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Computers in Biology and Medicine, vol. 121, no. April, pp. 1–11, 2020, doi: 10.1016/j.compbiomed.2020.103792.
 Z. Jiang et al., “Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device,” no. 19, 2020, [Online]. Available: http://arxiv.org/abs/2004.06912.
 T. A. E. da Silva, L. F. da Silva, D. C. Muchaluat-Saade, and A. Conci, “A computational method to assist the diagnosis of breast disease using dynamic thermography,” Sensors, vol. 20, no. 14, pp. 1–21, 2020, doi: 10.3390/s20143866.
 L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Reports, vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-76550-z.
 P. Jagadev and L. I. Giri, “Non-contact monitoring of human respiration using infrared thermography and machine learning,” Infrared Physics & Technology, vol. 104, no. January, p. 103117, 2020, doi: https://doi.org/10.1016/j.infrared.2019.103117.
 M. Farooq and A. Hafeez, “COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs,” arXiv, pp. 1–5, 2020, [Online]. Available: http://arxiv.org/abs/2003.14395.
 A. Ulhaq, A. Khan, D. Gomes, and M. Paul, “Computer Vision For COVID-19 Control: A Survey,” arXiv, pp. 1–24, 2020, [Online]. Available: http://arxiv.org/abs/2004.09420.
 A. V. Nguyen et al., “Comparison of 3 infrared thermal detection systems and self-report for mass fever screening,” Emerging Infectious Diseases, vol. 16, no. 11, pp. 1710–1717, 2010, doi: 10.3201/eid1611.100703.
 Y. Cho, S. Julier, N. Marquardt, and N. Bianchi-berthouze, “Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging Robust tracking of respiratory rate in high- dynamic range scenes using mobile thermal imaging,” Biomedical Optics Express, vol. 8, no. 10, pp. 4480–4503, 2017, doi: 10.1364/BOE.8.004480.