Revolutionizing Pneumonia Diagnosis: AI-Driven Deep Learning Framework for Automated Detection From Chest X-Rays

Pneumonia stands as a serious global health hazard that kills millions of lives annually, especially among susceptible populations such as the elderly and young children. Timely and accurate detection is paramount for initiating prompt intervention and improving patient prognoses. This article explores the transformative impact of deep learning on pneumonia diagnosis, emphasizing their pivotal role in revolutionizing the field. It specifically focuses on how these technologies are changing pneumonia diagnosis through intricate and advanced image analysis techniques. Using transfer learning with pre-trained models like ResNet50, MobileNetV2, AlexNet, EfficientNetB0, and Xception, the study focuses on automated pneumonia detection from X-ray images. It studies the efficacy of Contrast Limited Adaptive Histogram Equalization (CLAHE) and cross-validation techniques to enhance model performance. Results highlight the profound impact of deep learning models, with EfficientNetB0 consistently outperforming others, attaining test accuracy of 99.78% and perfect scores (100%) in precision, recall, F1-score, and 99.54% specificity. The study also highlights the importance of data preprocessing and rigorous evaluation methodologies in achieving remarkable accuracy in pneumonia detection. The study also highlights the importance of data preprocessing and rigorous evaluation methodologies in achieving remarkable accuracy in pneumonia detection. Our work shows superior performance in chest X-ray classification with other state-of-the-art models. Real-time analysis can be made possible by implementing these models in web-based or mobile apps, particularly in situations when resources are scarce or remote.