This paper presents AutoRespire, a multimodal AI-driven healthcare system designed for the detection of Tuberculosis and Pneumonia. The system integrates chest X-ray image analysis with clinical symptom data to improve diagnostic accuracy. Advanced machine learning and deep learning models, including Random Forest and Artificial Neural Networks, are utilized to analyze medical data and generate predictions. A late fusion strategy is applied to combine outputs from different models, enhancing reliability and performance. The proposed system demonstrates improved prediction accuracy and supports early diagnosis, especially in resource-limited settings. This approach aims to assist healthcare professionals in making faster and more accurate decisions for respiratory disease detection.
M et al. (Tue,) studied this question.