Comparative Analysis of Different Deep Learning Models to Classify Infectious Diseases from Chest Radiological Images

Abstract

Recent rapid advancement in deep learning has shown promise in manifold applications, with medical image analysis as one of its prime focus areas. A study was undertaken with the aim of comparing the effectiveness of traditional CNNs-VGG19, ResNet50, and a Custom-designed CNN architecture-with the newly evolving ViTs for categorizing chest radiological images of infectious respiratory conditions. Through an extensive EDA, we found that there are inherent challenges and complexities with medical imaging datasets. Addressing these challenges, tailored image pre-processing methodologies were used, emphasizing the importance of zoom and noise reduction in enhancing model efficacy. Our study findings have demonstrated the robustness and adaptability of CNNs, with VGG19, ResNet50, and Custom CNN outperforming the Vision Transformer on various performance metrics. However, besides accuracy, the importance of model interpretability was underlined. By applying gradient-based visualization and attention map methodologies, we tried to shed light on the "black box" nature of deep learning models and possibly open up new perspectives for enhancing cooperation between AI systems and healthcare professionals. This research underlines both the potential and challenges of AI in medical imaging and forms a foundation for further studies that conjoin technological innovation with clinical expertise.