Ali, Mudasir and Shahroz, Mobeen and Akram, Urooj and Mushtaq, Muhammad Faheem and Carvajal-Altamiranda, Stefanía and Aparicio Obregón, Silvia and Díez, Isabel De La Torre and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, stefania.carvajal@uneatlantico.es, silvia.aparicio@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model. IEEE Access, 12. pp. 34691-34707. ISSN 2169-3536
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Abstract
Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment.
| Item Type: | Article |
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| Uncontrolled Keywords: | Pneumonia detection, transfer learning, efficientnetv2l, data augmentation, chest X-rays |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 12 Apr 2024 07:24 |
| Last Modified: | 12 Apr 2024 07:24 |
| URI: | http://repositorio.funiber.org/id/eprint/11666 |
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