Influence of Input Image Size Variations and Data Balancing on VGG-16 and VGG-16-ELM Models for Pneumonia Classification - Rofiqul Zein, Mohammad Agil; Rahmat, Basuki; Puspaningrum, Eva Yulia (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
Pneumonia is a lung disease that can be identified through chest X-ray images. This study aims to evaluate the performance of two deep learning models, namely VGG-16 and a combination of VGG-16 with Extreme Learning Machine (ELM), in automatically classifying pneumonia. The approach used includes an analysis of variations in input image sizes (150×150, 200×200, 224×224, 256×256, and 300×300 pixels) as well as the application of data balancing techniques using Random Over Sampling (ROS). The dataset used contains 5,856 X-ray images classified into two classes: NORMAL and PNEUMONIA. The preprocessing stages include resizing, normalization, data splitting, and augmentation. Performance evaluation is conducted using metrics of accuracy, precision, recall, and F1-score. The experimental results show that the input size of 200×200 consistently yields the best performance. The VGG-16 model without the application of ROS achieved the highest accuracy of 96.59% and an F1-score of 97.69%. Meanwhile, the VGG-16-ELM combination showed significant performance improvement when ROS was applied. These findings indicate that the selection of model architecture, data balancing techniques, and input image size significantly influence classification accuracy, and contribute to the development of AI-based automated diagnostic systems. Keywords: Pneumonia, Deep Learning, VGG-16, Extreme Learning Machine, Data Balancing.