Comparison Of CNN, Resnet 50, And Vgg 16 For Pneumonia Classification Using Transfer Learning
TL;DR
The results showed that residual learning on ResNet50 effectively addressed the vanishing gradient problem in deep networks and achieved clinically relevant classification accuracy, supporting its potential integration into computer-aided diagnosis (CAD) systems.
Abstract
Pneumonia is one of the leading causes of death from infectious diseases worldwide, making rapid and accurate radiological diagnosis crucial for successful medical treatment. This study implemented and compared three deep learning architectures—a custom Convolutional Neural Network (CNN), ResNet50, and VGG16—for binary classification of chest X-ray images into Normal and Pneumonia categories. The Chest X-ray Pneumonia dataset from Kaggle (5,863 images) was used with an 80/10/10 (train/validation/test) data split and data augmentation to address class imbalance. ResNet50 with transfer learning from ImageNet weights achieved the best performance: 95.1% accuracy, 92.3% precision, 96.7% recall, 94.4% F1-score, and 97.5% AUC-ROC, outperforming the custom CNN (89.4% accuracy, 95.2% AUC) and VGG16 (93.7% accuracy, 96.1% AUC). Statistical analysis confirmed that the performance difference between ResNet50 and the custom CNN was statistically significant (p < 0.05). The results showed that residual learning on ResNet50 effectively addressed the vanishing gradient problem in deep networks and achieved clinically relevant classification accuracy, supporting its potential integration into computer-aided diagnosis (CAD) systems.