Pneumonia is a critical lung infection and a leading cause of morbidity and mortality worldwide. Early and accurate diagnosis is essential to ensure effective treatment and improved patient outcomes. Chest X-ray imaging, as a widely accessible diagnostic tool, presents challenges in manual interpretation due to overlapping anatomical structures and inter-observer variability. To address this, this study investigates the application of Convolutional Neural Networks (CNN) for automated pneumonia detection from chest X-ray images. The dataset used in this research consists of 5,863 labeled grayscale pictures obtained from the Kaggle repository, comprising 4,273 pneumonia and 1,583 normal cases. Preprocessing steps included image resizing, normalization, and class balancing through augmentation. The CNN model was trained using the augmented dataset and evaluated using various performance metrics. The proposed model achieved an overall accuracy of 79% on the test set, with a precision of 0.84, a recall of 0.79, and an F1-score of 0.78. The class-wise analysis revealed strong performance in detecting normal cases (F1-score = 0.82) but lower recall in pneumonia cases (Recall = 0.60), indicating a need for further improvement. In conclusion, CNN-based approaches demonstrate promising potential for aiding pneumonia diagnosis in clinical settings. However, additional work is necessary to enhance model reliability, particularly in detecting complex patterns of pneumonia. Future research may explore ensemble models and attention mechanisms to improve classification performance.
Salsabila et al. (Mon,) studied this question.