A SCALABLE REAL-TIME IOT AGRICULTURAL FARM SENSOR-BASED APPLICATION MONITORING SYSTEM

Pneumonia remains a critical global health challenge, causing significant mortality, particularly among vulnerable populations such as children, the elderly, and immunocompromised individuals. Early and accurate diagnosis is essential for effective treatment, yet traditional methods like manual chest X-ray analysis are time-consuming and reliant on expert interpretation, often leading to delays. This research introduces a deep learning-based pneumonia detection system utilizing Convolutional Neural Networks (CNNs), integrated with FastAPI for real-time inference. The system was trained on a large dataset of labeled chest X-ray images, employing data augmentation techniques to enhance generalization. The model achieved an accuracy of95.19%, with a precision of 98.38%, recall of 93.84%, F1-score of 96.06%, specificity of 97.43%, and an AUC of 0.9564, demonstrating robust differentiation between pneumonia and non-pneumonia cases. Deployed via FastAPI, the system enables seamless real-time predictions through a web-based interface, offering a scalable and clinically viable solution. This work advances medical image analysis by providing an accurate and efficient tool for pneumonia diagnosis. Future efforts will focus on improving model interpretability, expanding the dataset, and integrating the system into clinical workflows for broader adoption.