Artificial neural network for pneumonia disease detection based on chest x-ray photograph.

Pneumonia is a prevalent lung disease worldwide, and timely recognition is crucial. Machine learning has been increasingly applied in various fields, including healthcare, but it has limitations when dealing with a small dataset for training models. In this study, we focused on diagnosing pneumonia using X-ray images and developed an approach that employs the ANN algorithm. We used a small dataset of RGB images to test, train, and validate the ANN, and we tested the algorithm with 15 neurons. This paper examined 500 patient X-ray pictures from the Kaggle dataset for normal and abnormal cases. Ten thousand pixels were selected from these images, with 7,000 pixels allocated for training the ANN and 1,500 pixels each for testing and validation. The outcomes showed that the ANN with 15 neurons achieved remarkable performance on all performance indices, including specificity, F1-score, accuracy, sensitivity, and precision, with a score of 99.9%. Furthermore, the suggested approach outperformed the current solution for pneumonia diagnosis utilizing the ANN model. [ABSTRACT FROM AUTHOR]