Analisis Pengaruh Image Enhancement Pada Pendeteksian COVID-19 Berbasis Citra X-Ray

- Wahyono

doi:10.33633/tc.v22i1.7195

Analisis Pengaruh Image Enhancement Pada Pendeteksian COVID-19 Berbasis Citra X-Ray

- Wahyono

Abstract

Penyakit virus corona 2019 (COVID-19) merupakan keadaan darurat kesehatan masyarakat secara global. Salah satu cara untuk dapat mendeteksi adanya COVID-19 adalah dengan memanfaatkan citra x-ray dada yang mengidentifikasi anomali pada area paru-paru. Namun terkadang citra yang didapatkan pada melalui scan x-ray memiliki kualitas yang buruk sehingga sulit secara langsung untuk bisa dianalisis secara manual atau menggunakan model machine learning. Untuk menghasilkan analisis yang lebih baik, biasanya citra akan ditingkatkan terlebih dahulu kualitasnya dengan teknik image enhancement. Banyak metode image enhancement yang bisa dimanfaatkan untuk keperluan ini yang tentunya akan memberikan pengaruh yang berbeda-beda pada hasil pendeteksian COVID-19. Penelitian ini bertujuan untuk menganalisis pengaruh metode-metode image enhancement pada pendeteksian COVID-19 melalui citra x-ray. Metode image enhancement yang dianalisis adalah log transformation, constrast stretching, histogram equalization (HE), dan contrast limited adaptive histogram equalization (CLAHE). Model pendeteksian COVID-19 akan berdasarkan pada beberapa model machine learning antara lain convolutional neural network (CNN), k-nearest neighbor (KNN) dan support vector machine (SVM). Dari eksperiment didapatkan bahwa pengaplikasian image enhancement HE dan CLAHE memberikan peningkatan akurasi pada pendeteksian COVID-19 khususnya ketika diterapkan pada model CNN yaitu sebesar 1,13% dan 2,25%.

Keywords

image enhancement; covid19; citra x-ray; CNN; SVM; KNN

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DOI: https://doi.org/10.33633/tc.v22i1.7195