Transfer Learning for Pneumonia Detection

Abstract

Pneumonia is a serious respiratory condition that requires timely and accurate diagnosis to reduce morbidity and mortality rates. This paper presents a Transfer Learning-based approach for Pneumonia Detection using chest X-ray images. The proposed framework leverages pre-trained deep learning models such as Convolutional Neural Networks, which are fine-tuned on medical imaging datasets to improve diagnostic performance. Transfer learning enables the reuse of learned features from large-scale datasets, reducing the need for extensive labeled medical data and accelerating model training. Image preprocessing and augmentation techniques are applied to enhance model generalization and robustness. The system is evaluated using standard performance metrics, demonstrating high accuracy, sensitivity, and specificity in detecting pneumonia cases. Additionally, integration with prior research in machine learning and healthcare prediction strengthens the reliability of the model. The proposed approach provides a cost-effective and efficient solution for automated pneumonia screening, particularly in resource-constrained healthcare settings where expert radiological support may be limited.