ISSN 2278-9723
 

Research Article 


Machine Learning based Human eye disease interpretation

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI.

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.

Key words: Fundus; Hierarchical strategy; Picture matting method; Region properties; Trimap generation; Vessel partitioning.


 
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How to Cite this Article
Pubmed Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. Machine Learning based Human eye disease interpretation . . 2023; 11(2): 42-52. doi:10.31838/ijccts/11.02.07


Web Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. Machine Learning based Human eye disease interpretation . https://www.ijccts.org/?mno=122541 [Access: October 19, 2022]. doi:10.31838/ijccts/11.02.07


AMA (American Medical Association) Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. Machine Learning based Human eye disease interpretation . . 2023; 11(2): 42-52. doi:10.31838/ijccts/11.02.07



Vancouver/ICMJE Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. Machine Learning based Human eye disease interpretation . . (2023), [cited October 19, 2022]; 11(2): 42-52. doi:10.31838/ijccts/11.02.07



Harvard Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI (2023) Machine Learning based Human eye disease interpretation . , 11 (2), 42-52. doi:10.31838/ijccts/11.02.07



Turabian Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. 2023. Machine Learning based Human eye disease interpretation . International Journal of Communication and Computer Technologies, 11 (2), 42-52. doi:10.31838/ijccts/11.02.07



Chicago Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. "Machine Learning based Human eye disease interpretation ." International Journal of Communication and Computer Technologies 11 (2023), 42-52. doi:10.31838/ijccts/11.02.07



MLA (The Modern Language Association) Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI. "Machine Learning based Human eye disease interpretation ." International Journal of Communication and Computer Technologies 11.2 (2023), 42-52. Print. doi:10.31838/ijccts/11.02.07



APA (American Psychological Association) Style

MUHAMMAD NIZAM, SATINE ZANETA, FAISAL BASRI (2023) Machine Learning based Human eye disease interpretation . International Journal of Communication and Computer Technologies, 11 (2), 42-52. doi:10.31838/ijccts/11.02.07