Machine Learning based Human eye disease interpretation

Authors

  • MUHAMMAD NIZAM Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • SATINE ZANETA Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • FAISAL BASRI Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

Keywords:

Fundus, Hierarchical strategy, Picture matting method, Region properties, Trimap generation, Vessel partitioning

Abstract

In this section, a various levelled picture matting method is utilized to extract veins from fundus pictures. All the more explicitly, a various levelled methodology is joined into the picture matting strategy for vein apportioning. For the most part, the matting strategy requires a client indicated tri map, which isolates the info picture into three districts: the frontal area, foundation, and obscure areas. Be that as it may, producing a client indicated tri map is very tuff work for vessel parcelling undertakings. In this task, we propose a technique that creates tri map consequently by using area highlights of veins. At that point, we apply a various levelled picture tangling strategy to separate the vessel components in the obscure districts. The suggested technique has less count time and performs better than numerous other regulated and solo strategies. The datasets CHASE_DB1, STARE and DRIVE are adopted as these datasets are available openly. By applying these three datasets on pictures produces fixed time of 50.71, 15.74 and 10.72 seconds with optimized parcelling exactness 93.9%, 94.6% and 95.1%, respectively.

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Published

2023-05-20

How to Cite

NIZAM, M., ZANETA, S., & BASRI, F. (2023). Machine Learning based Human eye disease interpretation. International Journal of Communication and Computer Technologies, 11(2), 42–52. Retrieved from https://ijccts.org/index.php/pub/article/view/158

Issue

Section

Research Article