Performance Comparison of AlexNet and GoogLeNet on Pneumonia Chest X-Rays
Main Article Content
Keywords
Pneumonia, Chest-X-rays, CNN, AlexNet vs GoogleNet, Performance comparison
Abstract
Detecting pneumonia on chest radiographs is crucial for precise clinical intervention. Efficient diagnostic tools are necessary to assess a large number of X-rays and improve patient outcomes. This study uses 6,796 chest X-ray images, consisting of both pneumonia and healthy conditions, to test the classification performance of two deep learning models, GoogLeNet and AlexNet. Both models demonstrated high sensitivity; however, AlexNet exhibited higher accuracy, specificity, and F-score. AlexNet also showed a higher validation accuracy at 98.08% compared to GoogLeNet’s accuracy of 97.87%. Therefore, the results indicate that AlexNet model show better accuracy than the GoogLeNet model.
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References
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