Analisis Pengaruh Kompresi Citra Fundus terhadap Kinerja Sistem Automated Microanerysm Detections

  • Anugerah Galang Persada Universitas Gadjah Mada
  • Ahmad Nasikun Universitas Gadjah Mada
  • Igi Ardiyanto Universitas Gadjah Mada
  • Hanung Adi Nugroho Universitas Gadjah Mada
Keywords: Kompresi citra, diabetic retinopathy, microaneurysms detection, deep learning


Diabetes is one of the most serious diseases that commonly suffered by people around the world, including Indonesia. Early symptoms of diabetes could be observed from various indicators, one of which is through the retina. Retina conditions is affected by diabetics and when remain unproperly threated could lead to blindness. This retinal disorders due to diabetes is normally called Diabetic Retinopathy (DR). One method that able to distinguish and detect DR is microaneurysm detection. This method requires good quality of retinal images. However, in certain areas such as rural areas, this requirement may difficult to meet due to lack of adequate infrastructure. One solution that may overcome this problem is to compress the images. In this paper, image compression algorithms were applied to the retinal image, and then used to detect microaneuryms through Deep Learning-based systems. The result shows that the most stable and appropriate algorithm is PNG, which is able to correctly classify around 83% in accuracy with 5,5% variance.


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How to Cite
Anugerah Galang Persada, Ahmad Nasikun, Igi Ardiyanto, & Hanung Adi Nugroho. (2018). Analisis Pengaruh Kompresi Citra Fundus terhadap Kinerja Sistem Automated Microanerysm Detections . Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 7(1), 72-78. Retrieved from