Dynamic Lung Infection Detection System Using Deep Learning Algorithms on 3D CT Images: Modeling and Performance Evaluation

This paper presents a dynamic lung infection detection system utilizing advanced deep learning algorithms to analyze 3D CT images. The primary goal is to improve the accuracy and reliability of detecting lung infections, which is critical for providing timely medical intervention and enhancing patient outcomes. We investigated and compared two sophisticated models: a 3-dimensional Convolutional Neural Network (3D CNN) and a Residual Network (3D ResNet 101). These models were implemented in Python and rigorously evaluated on the MosMed dataset. The evaluation included key performance metrics such as accuracy, precision, recall, and F1 score, to assess their effectiveness in diagnosing lung infections from high-resolution 3D CT scans. The 3D CNN model demonstrated exceptional performance, achieving an accuracy of 99.60%, precision of 99.73%, recall of 96.80%, and an F1 score of 97.25%. In comparison, the 3D ResNet 101 model reached a maximum accuracy of 97.30%, precision of 99.25%, recall of 95.28%, and an F1 score of 96.32%. These results underscore the 3D CNN model’s superior performance in detecting lung infections. The study highlights the effectiveness of the 3D CNN model in lung infection detection, surpassing the 3D ResNet 101 model in several key performance metrics. This demonstrates that the integration of cutting-edge deep learning techniques with high-resolution 3D CT imaging offers significant advancements in diagnostic accuracy. The findings suggest that the 3D CNN model holds promise for enhancing diagnostic procedures and improving patient care in clinical settings. Future work will focus on further optimizing these models and exploring their applicability to other medical imaging tasks.