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Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture.

Md Alamin Talukder¹, Md Abu Layek¹, Mohsin Kazi²

¹Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.

Computers in Biology and Medicine

|December 2, 2023

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Deep learning models, particularly EfficientNetB4 fine-tuned on chest X-rays, show high accuracy in detecting COVID-19 and lung diseases. This offers a rapid and precise diagnostic aid for healthcare professionals.

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

The COVID-19 pandemic necessitates rapid and accurate disease detection.
Traditional genetic testing for COVID-19 is time-intensive and has limitations.
Radiological imaging, specifically chest X-rays, presents a promising alternative for diagnosis.

Purpose of the Study:

To investigate the efficacy of deep learning algorithms applied to chest X-rays for swift and precise COVID-19 detection.
To enhance diagnostic accuracy by fine-tuning transfer learning models.

Main Methods:

Utilized deep learning by fine-tuning established transfer learning models on a COVID-19 X-ray dataset (2000 images).
Evaluated models including Xception, InceptionResNetV2, ResNet50, ResNet50V2, EfficientNetB0, and EfficientNetB4.

Keywords:

COVID-19 Deep learning Diagnosis EffiecientNet Lung Transfer learning X-ray image

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Tested EfficientNetB4 on a separate Chest X-ray dataset (4,350 images) for general lung disease identification.

Main Results:

Achieved high accuracy rates across models, with EfficientNetB4 reaching 100% on the COVID-19 dataset.
Fine-tuned EfficientNetB4 demonstrated excellent performance in lung disease detection (99.17% accuracy, 99.14% F1-score).

Conclusions:

Fine-tuned transfer learning models, especially EfficientNetB4, are highly effective for rapid and accurate COVID-19 and lung disease detection using X-ray images.
This approach provides valuable support for radiologists and healthcare professionals in patient diagnosis.