Chest X-ray Based Pneumonia Diagnosis Using Deep Learning Technique

Pneumonia is a common respiratory illness. This study utilizes deep learning techniques to analyze chest X-ray images for pneumonia diagnosis, integrating four pre-trained feature extractors-DenseNet169, DenseNet201, MobileNet, and InceptionResNetV2-with classifiers like Random Forest, Support Vector Machine (SVM), and XGBoost. We employ various libraries for image processing, machine learning , deep learning (TensorFlow/Keras), and explainability methods like CLAHE and Laplacian filters to enhance images before model input. Our evaluation focuses on key metrics, including accuracy, precision, recall, F1-score, and AUC. The combination of DenseNet201 and Custom Classification has shown the highest accuracy in detecting pneumonia, highlighting its potential to improve diagnostic practices and healthcare outcomes. Overall, chest X-rays are highly effective for diagnosing pneumonia.