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International Journal of Computing Science and Applied Mathematics
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 24775401     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education Mathematics
(IJCSAM) International Journal of Computing Science and Applied Mathematics is an open access journal publishing advanced results in the fields of computations, science and applied mathematics, as mentioned explicitly in the scope of the journal. The journal is geared towards dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry.
Arjuna Subject : -
Articles 121 Documents
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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
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 9, No 2 (2023)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v9i2.15743

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

Page 13 of 13 | Total Record : 121

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