Klasifikasi COVID 19 dengan Metode EfficientNet berdasarkan CT scan Paru-paru

Authors

  • Akhmad Irsyad a:1:{s:5:"en_US";s:21:"Mulawarman University";}
  • Islamiyah Mulawarman University
  • Fakhmul Amal Mulawarman University

Abstract

Abstrak Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) adalah virus penyebab Covid-19. Covid-19 adalah virus mematikan yang oleh Organisasi Kesehatan Dunia (WHO) ditetapkan sebagai pandemi karena penyebarannya yang cepat. Dua metode yang kini paling sering digunakan untuk mendeteksi Covid-19 adalah Rapid Diagnostic Test (RDT) dan Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR). Menemukan strategi baru yang cepat dan tepat sangat penting karena kedua strategi memiliki kelebihan dan kekurangan. Penggunaan CT scan untuk menemukan Covid-19 adalah salah satu metode yang direkomendasikan. Makalah ini merekomendasikan identifikasi Covid-19 pada gambar CT menggunakan EfficientNet B0 tampil lebih unggul dari model tanpa CLAHE. Untuk performa EfficientNet B0 dengan CLAHE, akurasi, F-measure, recall, dan precision adalah 91,95%, 92,06%, 92,43%, dan 91,69%..   Kata kunci: Covid-19, Klasifikasi, Deep Learning, EfficientNet   Abstract [Classification of COVID-19 using the EfficientNet Method Based on Lung CT Scan] Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19. Covid-19 is a deadly virus that the World Health Organization (WHO) has designated as a pandemic due to its rapid spread. The two methods that are now most often used to detect COVID-19 are the Rapid Diagnostic Test (RDT) and Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR). Finding a new strategy that is quick and precise is crucial since both strategies have benefits and drawbacks. The use of a CT scan to locate Covid-19 is one recommended method. This paper recommends identifying COVID-19 on CT images using EfficientNet B0 performs superior to the model without CLAHE. For the performance of EfficientNet B0 with CLAHE, accuracy, F-measure, recall, and precision are 91.95%, 92.06%, 92.43%, and 91.69%.   Keywords: Covid-19, Classification, Deep Learning, EfficientNet

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Submitted

2023-07-25

Accepted

2024-03-25

Published

2024-03-25

Issue

Vol. 8 No. 2 (2023): November

Section

Articles

License

Copyright (c) 2024 Akhmad Irsyad, Islamiyah, Fakhmul Amal

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