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| Title | Ai-Driven Prioritization And Detection Of Covid-19 Cases In Smart City: An Integration Of X-Ray Imaging And Resnet-18 Machine Learning Model |
|---|---|
| ID_Doc | 7034 |
| Authors | Parasar D.; Kazi N.A.; Warule L.N.; Guru V. |
| Year | 2024 |
| Published | Healthcare-Driven Intelligent Computing Paradigms to Secure Futuristic Smart Cities |
| DOI | http://dx.doi.org/10.1201/9781032631738-11 |
| Abstract | In the contemporary era, smart cities are embracing advanced computing paradigms to address various challenges, healthcare being of prime importance. This chapter focuses on the integration of healthcare and smart city solutions during the COVID-19 pandemic. Amidst the crisis, conventional testing methods like (RT-PCR) were limited and time-consuming and showed limited sensitivity. A novel approach to overcome this predicament is by leveraging X-ray imaging to prioritize patients for RT-PCR testing. As X-ray machines are widely available and digitized, this methodology offers a swift and efficient solution. The cornerstone of our approach is the utilization of state-of-the-art machine learning (ML) techniques, particularly the ResNet-18 model, to automatically analyze X-ray scans for COVID-19 detection. Our approach seeks to triage patients, assisting in making crucial decisions about patient isolation and further testing. Moreover, this technique can help identify false-negative cases, thereby improving overall testing efficacy. This becomes exceedingly important in settings where professional radiologists are not readily available. On the publicly available COVID-19 chest X-ray dataset, our model has demonstrated a promising accuracy of 95.56% in detecting COVID-19 infection. The application of AI and ML in healthcare has been explored in this chapter, offering this model as a workable approach to handling medical emergencies in smart cities. By doing this, attempts are made to support the continuous work of developing intelligent computing paradigms driven by healthcare for safe and resilient smart cities of the future. © 2025 selection and editorial matter, Diptendu Sinha Roy, Mir Wajahat Hussain, K. Hemant Kumar Reddy, Deepak Gupta; individual chapters, the contributors. |
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