Leveraging Transfer Learning in Deep Convolutional Neural Networks for Pneumonia Diagnosis from Chest X-Rays

Pneumonia is a major worldwide health concern, particularly in areas of limited access to healthcare, causing considerable morbidity and mortality. Standard diagnosis is based on human interpretation of chest radiographs, a time- and labor-consuming task with variability and potential for errors. Deep learning, especially Convolutional Neural Networks (CNNs), has shown promise in computer-aided medical image analysis. This paper proposes a transfer learning-based CNN model for pneumonia detection, employing pre-trained architectures to achieve accuracy and handle data sparsity. The model is trained with publicly available chest X-ray images and fine-tuned to enhance sensitivity and specificity. For real-time use, it is implemented via a Flask-based web interface to enhance accessibility, particularly in resource-limited environments. Experimental results show better performance than conventional approaches, with higher precision, accuracy, and recall. The study identifies the potential of deep learning to improve diagnostic efficiency and patient outcomes.