Improved Accuracy in Automatic Detection of Pneumonia from Chest CT Images

Abstract

Pneumonia is a common and potentially life-threatening respiratory infection that often requires prompt diagnosis and treatment. Chest computed tomography (CT) imaging is a valuable tool for diagnosing pneumonia, but manual interpretation can be time-consuming and subjective. In recent years, machine learning algorithms have shown promise in automating the detection of pneumonia from chest CT images, aiming to improve diagnostic accuracy and efficiency. Magnetic Resonance Imaging (MRI): This study presents an improved approach for automatically detecting pneumonia from chest CT images using machine learning techniques. We propose a novel framework that combines advanced image processing methods with state-of-the-art deep learning architectures to enhance the accuracy of pneumonia detection. The proposed framework includes several key components: preprocessing steps for noise reduction and image enhancement, feature extraction methods to capture relevant patterns and textures, and a deep learning model trained on a large dataset of annotated chest CT scans. To evaluate the performance of our approach, we conducted extensive experiments using a diverse dataset of chest CT images collected from multiple medical centers. Our results demonstrate significant improvements in both sensitivity and specificity compared to existing methods, achieving high accuracy in pneumonia detection. Furthermore, we conducted extensive validation experiments and comparative analyses to validate the robustness and generalization capabilities of our approach across different patient populations and imaging protocols.