TY  - JOUR
KW  - Pneumonia detection
KW  -  transfer learning
KW  -  efficientnetv2l
KW  -  data augmentation
KW  -  chest X-rays
AV  - public
UR  - http://doi.org/10.1109/ACCESS.2024.3372588
A1  - Ali, Mudasir
A1  - Shahroz, Mobeen
A1  - Akram, Urooj
A1  - Mushtaq, Muhammad Faheem
A1  - Carvajal-Altamiranda, Stefanía
A1  - Aparicio Obregón, Silvia
A1  - Díez, Isabel De La Torre
A1  - Ashraf, Imran
Y1  - 2024/03//
TI  - Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model
EP  - 34707
VL  - 12
SN  - 2169-3536
SP  - 34691
N2  - 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.
JF  - IEEE Access
ID  - uninimx11666
ER  -