OPTIMIZATION OF COVID-19 DETECTION THROUGH TRANSFER LEARNING ON CHEST X-RAY IMAGES
DOI:
https://doi.org/10.33480/techno.v22i2.6172Keywords:
Chest X-Ray, Convolutional Neural Networks , COVID-19 , Deep Learning , Medical ImagingAbstract
The urgent need for rapid and reliable identification of COVID-19 cases has highlighted the importance of auxiliary diagnostic tools. Chest X-ray imaging serves as a key resource in clinical settings, yet manual interpretation remains susceptible to inter-observer variability and diagnostic delays. This study introduces an optimized deep learning framework based on transfer learning to enhance the detection of COVID-19 from chest X-ray images. The aim is to improve classification accuracy and operational efficiency using pre-trained models tailored for radiographic analysis. We applied transfer learning with fine-tuning to convolutional neural networks pre-trained on large-scale image datasets. The models were adapted and evaluated on a curated collection of chest X-rays representing COVID-19 positive and negative cases. The proposed model achieved a test accuracy of 99% with a loss of 0.15, indicating high diagnostic performance and robustness in distinguishing COVID-19 cases from other pulmonary conditions. Transfer learning offers a viable and efficient strategy for COVID-19 screening using chest X-rays. This approach has the potential to support frontline clinical decision-making and scale public health response during outbreaks.
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