Enhanced Pneumonia Detection Using Deep Learning Techniques on Chest X-Rays

Pneumonia is one of the leading causes of morbidity and mortality all over the world. Its early and appropriate diagnosis is extremely important for effective management. Traditional detection of pneumonia from chest X-rays heavily relies on the proficiency of expert radiologists, which is very time-consuming and prone to variability. This article presents an advanced deep learning approach for enhanced capabilities in pneumonia detection from chest X-ray images. This paper proposes a new CNN architecture optimized by extensive hyperparameter tuning and data augmentation techniques for improving accuracy and robustness in pneumonia detection. Our model was trained and validated on a large labeled dataset of chest X -ray images, outperforming conventional methods in detection performance. The results have shown improved accuracy, sensitivity, and specificity in the detection. We also discuss how different deep learning methods, such as transfer learning and ensemble methods, affect the performance of the models. The current study has shown the potential of deep learning for the automation and augmentation of pneumonia diagnosis, thus fostering the development of more efficient and reliable diagnostic tools in clinical practice.