Wisnumurti, Garda (2021) Deteksi COVID-19 Dengan Data Medis Chest X-Ray Menggunakan Deep Learning Model Dan Machine Learning. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sejak wabah COVID-19 di Wuhan, Provinsi Hubei, China pada Desember 2019, sudah menyebar dalam waktu yang singkat. Satu bulan kemudian, pada 30 Januari 2020, Organisasi Kesehatan Dunia (WHO) mengumumkan bahwa COVID-19 adalah darurat kesehatan global. Saat ini, deteksi COVID-19 menggunakan reverse transcriptase polymerase chain reaction (RT-PCR) untuk mendeteksi asam nukleat virus merupakan deteksi standar untuk mendiagnosis COVID-19. Namun, hasil deteksi COVID-19 menggunakan RT-PCR sering kali membutuhkan waktu yang lama dari berjam-jam hingga berhari-hari.
Studi menunjukkan bahwa deteksi COVID-19 menggunakan data medis chest X-ray memerlukan waktu yang lebih singkat dan memiliki sensitivitas yang lebih tinggi sehingga dapat digunakan sebagai alternatif dari reverse transcriptase polymerase chain reaction (RT-PCR). Dalam tugas akhir ini diimplementasikan kombinasi deep learning model dan machine learning classifier untuk mendeteksi COVID-19 menggunakan data medis chest X-ray.
Hasil eksperimen menunjukkan bahwa arsitektur VGG16 dengan classifier Support Vector Machine lebih baik daripada metode CNN dengan arsitektur VGG16 saja yang menghasilkan akurasi sebesar 100%. Studi ini diharapkan dapat membantu ahli radiologi dalam mendeteksi COVID-19 untuk mempercepat proses penanganan COVID-19.
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Since the Covid-19 outbreak in Wuhan, Hubei Province, China in December 2019, has spread in a short time. One month later, on January 30, 2020, the World Health Organization (WHO) announced that Covid-19 was a global health emergency. At present, Covid-19 detection uses reverse transcriptase polymerase chain reaction (RT-PCR) to detect viral nucleic acid is a standard detection to diagnose Covid-19. However, the results of Covid-19 detection using RT-PCR often takes a long time from hours to days.
Studies show that Covid-19 detection uses Chest X-ray medical data requires a shorter time and has a higher sensitivity so that it can be used as an alternative to reverse transcriptase polymerase chain reaction (RT-PCR). In this final project, the combination of the Deep Learning Model and Machine Learning Classifier to detect Covid-19 using Chest X-Ray medical data.
The experimental results show that the VGG16 architecture with the Support Vector Machine classifier is better than the CNN method with the VGG16 architecture alone which produces an accuracy of 100%. This study is expected to assist radiologists in detecting COVID-19 to accelerate the process of handling COVID-19.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | COVID-19, Support Vector Machine, CNN, Deep Learning, Machine Learning, Chest X-Ray |
| Subjects: | Q Science > QA Mathematics > QA336 Artificial Intelligence R Medicine > R Medicine (General) > R858 Deep Learning R Medicine > RA Public aspects of medicine > RA644.C67 COVID-19 (Disease) |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Garda Negara Wisnumurti |
| Date Deposited: | 22 Aug 2021 02:25 |
| Last Modified: | 09 Jul 2024 03:33 |
| URI: | http://repository.its.ac.id/id/eprint/88352 |
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