Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images

Abstract

This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.