A Review on Covid-19 Detection Using Artificial Intelligence from Chest CT Scan Slices
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Dhanshri Mali [1] ; S. A. Patil [2]
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[1] Department of Electronics Engineering, DKTE Ichalkarnji Research Center, Shivaji Univesity, Kolhapur
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[2] Head of Department Electronics Engineering, DKTE Ichalkarnji, Shivaji Univesity, Kolhapur
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- Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 13, Nº. 1, 2024
- Idioma: inglés
- Enlaces
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Resumen
The outbreak of COVID-19, a contagious respiratory disease, has had a significant impact on people worldwide. To prevent its spread, there is an urgent need for an easily accessible, fast, and cost-effective diagnostic solution. According to studies, COVID-19 is frequently accompanied by coughing. Therefore, the identification and classification of cough sounds can be a promising method for rapidly and efficiently diagnosing the disease. The COVID-19 epidemic has resulted in a worldwide health crisis, and stopping the disease's spread depends on a quick and precise disease diagnosis. COVID-19 has been detected using medical imaging modalities such as chest X-rays and computed tomography (CT) scans due to their non-invasive nature and accessibility. This research provides an in-depth examination of deep learning-based strategies for recognising COVID-19 in medical images. The benefits and drawbacks of various deep learning approaches and their applications in COVID-19 detection are discussed. The study also examines publicly available datasets and benchmarks for evaluating deep learning model performance. Furthermore, the limitations and future research prospects for using deep learning in COVID-19 detection are discussed. This survey's goal is to offer a comprehensive overview of the current state of advancement in deep learning-based COVID-19 detection using medical images. This can aid researchers and healthcare professionals in selecting appropriate approaches for an effective diagnosis of the disease.
