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- W4383753014 abstract "One of the most common causes of pneumonia is an infection caused by bacteria, viral or fungal organisms. Regarding children’s deaths, pneumonia is the leading infectious cause of mortality. The most common diagnostic tool for pneumonia is a chest X-ray which is exceedingly problematic due to various lung disorders, such as volume loss, haemorrhage, fluid overload, lung cancer, or post-radiation or surgical alterations. Intelligent systems are gaining popularity in diagnosing; however, developing a faster method that performs better in terms of accuracy is mandatory. In this paper, we take into account the Chest X-ray dataset, which accounts for the image classification task, and modify it to make it compatible with object detection. We utilize a lightweight YOLOv4-based model and assess it using our modified pneumonia detection dataset. Our model deals with three categories of X-ray images– viral pneumonia, bacterial pneumonia, and normal. Comparing our method with the existing systems proves the supremacy of our proposed method." @default.
- W4383753014 created "2023-07-11" @default.
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- W4383753014 date "2023-01-01" @default.
- W4383753014 modified "2023-09-25" @default.
- W4383753014 title "A Lightweight Object Detection Model to Detect Pneumonia Types" @default.
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- W4383753014 doi "https://doi.org/10.1007/978-981-99-2322-9_45" @default.
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