International Journal For Multidisciplinary Research

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A Comparative Study of YOLO Models For Pneumonia Detection

Author(s) Mohammed Saifuddin Munna, Rahul Chowdhury, Asif Mohammed Siddiqee
Country Bangladesh
Abstract Traditional pneumonia detection methods are usually based on chest X-rays processed with the help of experienced radiologists, allowing for slow processing times and considerable bias. It can have the greatest of implications on the outcome of pneumonia that is a leading killer of children when its timely diagnosis is essential for proper treatment. For accelerated and automated pneumonia identification, deep learning is a prospective technique. Even though different techniques are suggested, some studies proved object detection models to be promising for disguise detection. In this study, we compared three YOLO models (YOLOv3, YOLOv4, and YOLOv6) to determine their performances in detecting pneumonia. We use a dataset of three-class chest X-rays for which we are asked to categorize chest X-rays into normal, viral pneumonia, and bacterial pneumonia. Here, we investigate the performance of different YOLO models to detect pneumonia and classify the type of pneumonia. We aim to demonstrate that the results show the superiority of YOLOv6 over YOLOv3 and YOLOv4, which may help speed up and improve the pneumonia identification in the clinic, ultimately supporting for early intervention and better patient prognosis.
Keywords Pneumonia, YOLOv3, YOLOv4, Deep Learning, YOLOv6
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-17
Cite This A Comparative Study of YOLO Models For Pneumonia Detection - Mohammed Saifuddin Munna, Rahul Chowdhury, Asif Mohammed Siddiqee - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.22770
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.22770
Short DOI https://doi.org/gt2b8k
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