PNEUMONIA DETECTION USING DEEP LEARNING

Abstract

This paper introduces an advanced deep learning approach for pneumonia detection using convolutional neural networks (CNNs). Trained on a large dataset of annotated chest X-ray images, our model leverages transfer learning and addresses class imbalance through data augmentation and weighted loss-functions. Visualization techniques, including Grad-CAM, enhance interpretability, aiding clinicians in understanding the model's focus. Evaluation on a benchmark dataset demonstrates superior sensitivity and specificity compared to traditional methods. Our findings highlight the model's robustness across diverse demographics, emphasizing its potential for early diagnosis and improved patient outcomes. The study underscores the transformative impact of deep learning on pneumonia diagnosis, providing a valuable tool for efficient and accurate healthcare practices.