Klasifikasi Pneumonia Dengan Deep Learning Faster Region Convolutional Neural Network Arsitektur VGG16 dan ResNet50
DOI:
https://doi.org/10.22441/incomtech.v12i3.15112Kata Kunci:
Klasifikasi Pneumonia, COVID-19, Faster R-CNN, Convolutional Neural NetworkAbstrak
Pneumonia merupakan infeksi paru-paru yang melibatkan alveoli (kantung udara) dan disebabkan oleh mikroba, termasuk bakteri, virus, atau jamur yang dapat menyebabkan peradangan pada area bronkiolus dan alveoli. Pneumonia merupakan penyakit yang berbahaya apabila tidak ditangani dengan tepat. COVID-19 merupakan virus baru yang menyerang paru-paru dan diindikasikan dengan adanya pneumonia. Penting bagi pakar kesehatan untuk memberikan perawatan tepat jika ditemukan pneumonia pada kasus COVID-19. Namun demikian, kendala yag dihadapi dari citra toraks adalah mendeteksi adan-ya indikasi pneumonia dalam mengklasifikasikan toraks berpneumonia. Tujuan dari penelitian ini adalah mengklasifikasikan objek yang terdeteksi sebagai penumonia dengan menggunakan Faster R-CNN, yakni teknik yang mengkombinasikan algoritme Region Proposal Network (RPN) dan Convolutional Neural Network (CNN). Penelitian ini menggunakan metode Faster R-CNN untuk mendeteksi adanya pneumonia pada pasien COVID-19 dengan menggunakan dua arsitektur CNN yang berbeda yaitu arsitektur VGG16 dan ResNet50. Dari pengujian yang yang diterapkan pada citra toraks berpneumonia, model VGG16 mempunyai mAP (mean Average Precision) tertinggi yaitu sebesar 17,7% sedangkan ResNet mempunyai nilai mAP sebesar 16,2%. Sedangkan, implementasi menggunakan 500 data x-ray paru-paru pneumonia COVID-19, arsitektur VGG16 mempunyai nilai akurasi tertinggi yaitu sebesar 85,8% sedangkan ResNet50 mempunyai nilai akurasi sebesar 84%. Dengan dikembangkannya penelitian ini diharapkan dapat membantu tenaga medis dalam mendeteksi pneumonia secara dini pada pasien yang terkena virus COVID-19 dengan tepat.
Unduhan
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to UMB. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the UMB copyright notice are indicated, the copies are not used in any way that implies UMB endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
