Optimizing Pneumonia Identification in Chest X-Rays Using Deep Learning Pre-Trained Architecture for Image Reconstruction in Medical Imaging DOI Open Access
Rajshri C. Mahajan

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 52 - 63

Published: April 2, 2025

The rapid accumulation of fluid in the lungs is hallmark fatal illness known as pneumonia. Therefore, it crucial to get a diagnosis and medication soon possible order stop condition from getting worse. In diagnose pneumonia, chest X-rays (CXR) are typically used. This study assesses efficacy ResNet50 other pre-trained DL models classifying X-ray images evidence proposed model achieves an accuracy 93.06%, precision 88.97%, recall 96.78%, F1-score 92.71%, surpassing MobileNet, EfficientNetB0, Xception across all performance metrics. has been further tested shown be reliable effective differentiating between normal pneumonia patients utilizing ROC curve analysis, accuracy-loss trends, confusion matrix. highlights superiority automated detection, offering promising tool for early clinical decision support. results highlight how deep learning-based methods have ability improve radiological evaluations, which turn can decrease diagnostic mistakes increase patient outcomes. research contributes developing AI-driven medical imaging solutions, facilitating more accurate scalable detection real-world healthcare settings

Language: Английский

Optimizing Pneumonia Identification in Chest X-Rays Using Deep Learning Pre-Trained Architecture for Image Reconstruction in Medical Imaging DOI Open Access
Rajshri C. Mahajan

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 52 - 63

Published: April 2, 2025

The rapid accumulation of fluid in the lungs is hallmark fatal illness known as pneumonia. Therefore, it crucial to get a diagnosis and medication soon possible order stop condition from getting worse. In diagnose pneumonia, chest X-rays (CXR) are typically used. This study assesses efficacy ResNet50 other pre-trained DL models classifying X-ray images evidence proposed model achieves an accuracy 93.06%, precision 88.97%, recall 96.78%, F1-score 92.71%, surpassing MobileNet, EfficientNetB0, Xception across all performance metrics. has been further tested shown be reliable effective differentiating between normal pneumonia patients utilizing ROC curve analysis, accuracy-loss trends, confusion matrix. highlights superiority automated detection, offering promising tool for early clinical decision support. results highlight how deep learning-based methods have ability improve radiological evaluations, which turn can decrease diagnostic mistakes increase patient outcomes. research contributes developing AI-driven medical imaging solutions, facilitating more accurate scalable detection real-world healthcare settings

Language: Английский

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