EfficientNet-B0-Based Classification of Pneumonic Lungs Using Chest X-Ray Dataset

In this research paper, the authors take a new angle: classify pneumonic lungs by utilizing up-to-date architecture such as EfficientNet-BO. Given a huge X-Ray dataset of real cases, the model is excellent at identifying pneumonia. The study aims to address a crucial need in the area of respiratory medicine-through development and application for clinical diagnosis of diseases of the lungs which are efficient, accurate and can be used on everyone. Its strong ability to learn is shown as the model demonstrates an awesome 99.50% training accuracy, checks itself out by verifying results with a validation accuracy of 92.20%, and sits tight under pressure during testing with an accuracy at 99.28%. As a matter of fact, these results serve to remind us that this model is excellent in accurately identifying normal and pneumonic lung conditions. This research not only enriches the growing literature on deep learning techniques in medical image analysis but also offers an extremely valuable tool for health care providers which can rapidly and precisely diagnose pneumonia. The method uses the EfficientNet-B0 architecture, known for its high performance in image classification. The model is built from a rich and diverse Chest X-Ray dataset, ensuring that it can be generalized to different patient populations and conditions of imaging. The study’s findings hold promise for improving diagnostic accuracy, lowering human error as well as ultimately transforming patient outcomes in the realm of pneumonia detection by means automated image classification.