Combined VGG-Long Short Term Memory with Gamma Correction for Pneumonia Type Classification based on Chest X-Rays

Nia Amelia, Riskyana Dewi Intan Puspitasari, Hasanuddin Al-Habib, Elly Matul Imah

Abstract


Pneumonia is the second disease with the most patients being treated in the emergency department. Pneumonia can be distinguished based on its severity into viral and bacterial pneumonia. The coronavirus (COVID-19) has become a pandemic that spread globally. Panic during the pandemic has caused many people to self-diagnose and acknowledge common pneumonia as COVID-19. Despite having almost similar symptoms, not all pneumonia is COVID-19. Pneumonia is an inflammation of the lungs caused by bacteria, viruses, or fungi. In contrast, pneumonia in COVID-19 is caused by the SARS-CoV-2 virus. Early diagnosis of COVID-19 and pneumonia is crucial to perform the optimal treatment. A chest x-ray is a common way to detect pneumonia and is recommended for COVID-19. This study proposes a Pneumonia classification including a COVID-19 system based on X-Rays images using VGG-long short-term memory (LSTM) on chest X-ray images. This study applied gamma correction image enhancement to the thorax X-ray image. In the proposed system, VGG is used for feature extraction, and LSTM is used as a classifier. The experimental results show that the proposed system got an accuracy of 96.88% compared to previous state-of-the-art methods for pneumonia classification


Keywords


Pneumonia; Covid-19; VGG; LSTM; Classification

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References


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DOI: http://dx.doi.org/10.12962/j24775401.v9i2.15743

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