A Comprehensive Multimodal Analysis for Detecting Pulmonary Infiltrates in Chest X-Ray Image

This research paper delves into the critical task of detecting pulmonary infiltrates indicative of pneumonia in Chest X-ray images through a comprehensive multimodal analysis. This study focuses on evaluating the performance of a diverse set of models on Chest X-ray datasets, offering a comparative analysis and contribute meaningfully to pneumonia detection advancement through leveraging cutting-edge models, it's essential to scrutinize the strengths and limitations of each model. By employing rigorous evaluation criteria, we can discern which models offer the most promise in terms of accuracy, efficiency, scalability, and interpretability. The dataset used for experimentation encompasses a broad spectrum of chest X-ray images, ensuring a representative sample that captures the nuances of real-world clinical scenarios. The proposed multimodal analysis considers not only the visual features present in the X-ray images but also incorporates textual and contextual information. This holistic approach aims to enhance the overall diagnostic accuracy by leveraging complementary modalities. The results obtained from the experiments shed light on the strengths and weaknesses of the selected models, paving the way for informed decisions in deploying pneumonia detection systems. This research contributes to the evolving landscape of medical image analysis by presenting a comprehensive multimodal approach for detecting pulmonary infiltrates in Chest X-ray images. The comparative study not only aids in identifying optimal models for pneumonia detection but also highlights the potential for improved diagnostic accuracy.