Deep Learning-Based Diagnosis of Pneumonia Using Convolutional Neural Networks

Abstract

Pneumonia is a respiratory illness characterized by lung inflammation, often caused by pathogens such as viruses, bacteria, or fungi. Timely detection of pneumonia is crucial for effective treatment. While chest X-rays are commonly used for diagnosis, manual interpretation can be time-consuming, especially in areas with limited access to trained radiologists. Recently, deep learning models have emerged as an efficient method for pneumonia diagnosis. Many researchers are dedicated to enhancing the capabilities of pneumonia diagnosis through artificial intelligence methods. This study employs a convolutional neural network (CNN) for pneumonia diagnosis using an X-rays dataset from healthy individuals and those affected by pneumonia. The model achieved an accuracy of 59.9%, a precision of 77.75%, a recall of 59.9%, and an F1 score of 52.21% on the test dataset. Further tuning of the model’s hyperparameters is necessary to improve performance metrics.