| Autor: |
Subramanian, R. R., Natraj, C., Sudharsan, R. R., Hariharasitaraman, S., Priyadharsan, M. R. T., Kamalakannan, P., Jayakumar, S., Ishaq, A. M. S., Padmanaban, S. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3161 Issue 1, p1-16, 16p |
| Abstrakt: |
Pneumonia is a common respiratory ailment impacting millions worldwide. It stems from bacterial, viral, or fungal infections and can escalate into a serious, life-threatening condition if not promptly addressed. As per the World Health Organization (WHO), pneumonia stands as the leading cause of death among children under five globally, contributing to nearly 15% of all fatalities in this age group. Moreover, pneumonia accounts for 15% of all hospitalizations in theUnited States and is a prominent cause of death globally, especially in low-income nations. Pneumonia diagnosis must be preciseand prompt for optimal treatment and patient outcomes. Deep learning algorithms have developed as a useful tool for analyzing and classifying medical pictures, especially chest X-rays used to detect pneumonia, in recent years. The correct categorization of pneumonia into several categories can assist healthcare providers in making educated decisions about patient care and treatment. We can increase the accuracy and efficiency of pneumonia diagnosis using deep learning approaches, potentially leading to betterpatient outcomes. The study emphasizes the promise of deep learning techniques in medical image processing, as well as their ability to enhance healthcare outcomes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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