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Pneumonia Detection in Chest X-ray Images using ResNet50 Model
Pneumonia is the leading cause of death in children under the age of five worldwide. Chest X-rays are examined by trained radiologists to diagnose pneumonia. This procedure, however, is tedious and time-consuming. In medical image examination, biomedical image diagnosis techniques have a lot of potential. This paper proposes a model for identifying pneumonia that is trained on chest X-ray images. The ResNet50 model has been used to detect the presence of this ailment which may aid radiologists in their clinical decision-making by assisting them in detecting disease. The model was tested for overfitting and generalization errors and statistically validated. To assess the efficacy of the proposed model, various scores were computed, including testing accuracy, F1, recall, precision, and AUC score. On the pneumonia dataset, the proposed model had a test F1 score of 86%.
Pneumonia Detection in Chest X-ray Images using ResNet50 Model
Pneumonia is the leading cause of death in children under the age of five worldwide. Chest X-rays are examined by trained radiologists to diagnose pneumonia. This procedure, however, is tedious and time-consuming. In medical image examination, biomedical image diagnosis techniques have a lot of potential. This paper proposes a model for identifying pneumonia that is trained on chest X-ray images. The ResNet50 model has been used to detect the presence of this ailment which may aid radiologists in their clinical decision-making by assisting them in detecting disease. The model was tested for overfitting and generalization errors and statistically validated. To assess the efficacy of the proposed model, various scores were computed, including testing accuracy, F1, recall, precision, and AUC score. On the pneumonia dataset, the proposed model had a test F1 score of 86%.
Pneumonia Detection in Chest X-ray Images using ResNet50 Model
Barneih, Feras (author) / Nasir, Nida (author) / Kansal, Afreen (author) / Alshaltone, Omar (author) / Bonny, Talal (author) / Al-Shabi, Mohammad (author) / Al-Shammaa, Ahmed (author)
2023-02-20
453088 byte
Conference paper
Electronic Resource
English
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