An Efficient Convolutional Neural Network Model for Pneumonia Detection in Chest X-ray Images

Pneumonia is a lung infection that causes alveoli to become swollen and filled with liquid or pus. Each year pneumonia contributes to an increased mortality rate. The proposed work has been implemented to early diagnose pneumonia to decrease the mortality rate while saving precious lives. The findings of this study show that the Convolutional Neural Network (CNN) model successfully diagnoses pneumonia in chest X-ray images, with a test accuracy and test loss of 93.75% and 0.2797, respectively. A model with good performance has been made through careful selection of a diverse and well-preprocessed dataset, as well as with deep hyperparameter tuning. This performance is also evaluated on a wide range of evaluation measures, such as precision, recall, and F1-score, which constantly show that the model has resulted in the difference between cases with pneumonia and normal lungs. This model can also be used in clinical practice in solving real-life problems in healthcare.