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Title |
Enhancing Pneumonia Detection Using Deep Learning |
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Author |
Amal Alshahrani, Batul Mrakkan, Hala Algethami, Hanan Alhaj, Jana Hariri, Waseelah Alamoudi |
| Citation |
Vol. 25 No. 5 pp. 183-193 |
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Abstract |
Pneumonia represents a significant public health challenge. This condition, caused by bacteria, viruses, or fungi, leads to the soreness of the air sacs in one or both lungs, resulting in health issues ranging from mild to life-threatening. The severity of pneumonia needs timely and accurate diagnosis for effective treatment, especially for some cases such as young children, the elderly, and individuals with weakened immune systems or pre-existing health conditions. Neural networks, particularly in the field of medical imaging analysis, have improved the way healthcare professionals diagnose and treat conditions. Neural networks are inspired by the human brain's structure and function, enabling them to learn from vast amounts of data and identify complex patterns that may not be immediately apparent to human observers. We aim to introduce an approach employing advanced deep learning technologies and algorithms, specifically designed to improve the accuracy and efficiency of pneumonia detection, to help healthcare specialists, and to save as many innocent lives as possible. |
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Keywords |
Deep Learning, Pneumonia, Artificial Intelligence, Classification |
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