Citation :

Adel Rajab, "A Deep Transfer Learning Model for Robust Pneumonia Detection from Medical Imaging," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 3, pp. 11-24, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I3P102

Abstract

Pneumonia is a serious infection of the lungs brought about by several types of bacteria and viruses. It is usually difficult to detect and treat with chest X-rays because its visual patterns often overlap those of other pulmonary diseases. In recent years, deep learning has shown considerable success in medical image processing, such as the fully automated detection and classification of diseases with high accuracy. These models can identify complex patterns from large datasets, hence finding their perfect applications in radiology. This work proposes a deep learning-based pneumonia detection approach using transfer learning models. Images were obtained from an updated, publicly available version of the Paul Pulmonary Chest X-ray dataset. To extract meaningful features, a pretrained DenseNet121 network was utilized. Various transfer learning architectures, such as VGG16, ResNet50, InceptionV3, and DenseNet121, were trained and tested in this work. All architectures employed a unified MLP classification head to refine the extracted features and generate the final prediction. Model performance metrics include accuracy, precision, recall, F1 score, confusion matrix, AUC, and loss curves. Among them, DenseNet121 produced the highest accuracy of 89% and yielded an AUC of 0.96. The findings have shown that deep learning models, especially DenseNet121, can effectively detect pneumonia from chest X-rays, hence providing a very important tool for the radiologist and healthcare professional to improve both the speed and accuracy of diagnosis.

Keywords

Pneumonia, Chest X-ray, Transfer Learning Models, Feature Extractions.

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