Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model
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 c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10458135/ |
| _version_ | 1797243386103595008 |
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| author | Mudasir Ali Mobeen Shahroz Urooj Akram Muhammad Faheem Mushtaq Stefania Carvajal Altamiranda Silvia Aparicio Obregon Isabel De La Torre Diez Imran Ashraf |
| author_facet | Mudasir Ali Mobeen Shahroz Urooj Akram Muhammad Faheem Mushtaq Stefania Carvajal Altamiranda Silvia Aparicio Obregon Isabel De La Torre Diez Imran Ashraf |
| author_sort | Mudasir Ali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:54:17Z |
| format | Article |
| id | doaj.art-b7e818c69d874420a0bba73b89da8335 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:17Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b7e818c69d874420a0bba73b89da83352024-03-26T17:46:52ZengIEEEIEEE Access2169-35362024-01-0112346913470710.1109/ACCESS.2024.337258810458135Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L ModelMudasir Ali0Mobeen Shahroz1Urooj Akram2Muhammad Faheem Mushtaq3https://orcid.org/0000-0003-1922-6205Stefania Carvajal Altamiranda4Silvia Aparicio Obregon5Isabel De La Torre Diez6https://orcid.org/0000-0003-3134-7720Imran Ashraf7https://orcid.org/0000-0003-1922-6205Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, PakistanUniversidad Europea del Atlántico, Santander, SpainUniversidad Europea del Atlántico, Santander, SpainDepartment of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Valladolid, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, South KoreaPneumonia 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.https://ieeexplore.ieee.org/document/10458135/Pneumonia detectiontransfer learningefficientnetv2ldata augmentationchest X-rays |
| spellingShingle | Mudasir Ali Mobeen Shahroz Urooj Akram Muhammad Faheem Mushtaq Stefania Carvajal Altamiranda Silvia Aparicio Obregon Isabel De La Torre Diez Imran Ashraf Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model IEEE Access Pneumonia detection transfer learning efficientnetv2l data augmentation chest X-rays |
| title | Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model |
| title_full | Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model |
| title_fullStr | Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model |
| title_full_unstemmed | Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model |
| title_short | Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model |
| title_sort | pneumonia detection using chest radiographs with novel efficientnetv2l model |
| topic | Pneumonia detection transfer learning efficientnetv2l data augmentation chest X-rays |
| url | https://ieeexplore.ieee.org/document/10458135/ |
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