An Investigation into Detecting Pneumonia Through Image Processing and Object Detection

 Paper Title

An Investigation into Detecting Pneumonia Through Image Processing and Object Detection 
Authors
Prithvi Sairaj Krishnan, United States of America
Abstract
One of the most common respiratory infections that causes substantial morbidity and mortality worldwideis pneumonia, particularly in poorer countries with poor medical infrastructure. Chest X-ray imaging is essential for early diagnosis, although it can be difficult. In order to identify pneumonia from chest X-rays, this study created an automated deep learning computer-aided diagnosis method. Three pre-trained convolutional neural network models (ResNet-18, DenseNet-121), together with a newly developed weighted average ensemble approach based on evaluation metric scores, were used in the ensemble. Tested using five-fold cross-validation on two public X-ray datasets for pneumonia, the methodoutperformed stateof-the-art techniques with high accuracy (98.2%, 86.7%) and sensitivity (98.19%, 86.62%). Over 2.5 million fatalities globally are attributed to pneumonia each year. This precise automated model can help radiologists diagnose patients in a timely manner, particularly in situations with limited resources. How it is included into clinical decision assistance systems has the potential to improve pneumonia management and outcomes significantly.
Pdf URL: https://aircconline.com/ijait/V14N5/14524ijait01.pdf
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