Deep Learning Enabled Pneumonia Detection From Chest X-rays: A Transfer Learning Based Ensemble Classification Approach

Deep learning has revolutionized medical image analysis, leading to advancements in diagnosis and treatment. This article examines the application of deep learning techniques, specifically convolutional neural networks (CNNs), for diagnosing pneumonia in chest X-rays. Utilizing transfer learning algorithms and ensemble methods, the proposed method aims to improve feature extraction accuracy and automated pneumonia identification techniques. The research compares three transfer learning models—DenseNet169, VGG16, and InceptionV3—and employs ensemble techniques to generate more precise predictions. The architecture of the ensemble model combines the probabilistic outputs of each base model using a soft voting layer, resulting in enhanced predictive performance. Deep learning and ensemble approaches can potentially improve pneumonia detection from chest X-ray images, as demonstrated by these findings.