@inproceedings{23276c558d994bab9fc4a29ab8a51b29,
title = "Retinal Blood Vessel Segmentation using Random Forest with Gabor and Canny Edge Features",
abstract = "Recent developments in machine learning increases the researcher's interest in processing the medical images in diagnosis. The medical field requires precise diagnosis to detect the disease. This paper proposed a fusion of features that are extracted from canny edge detector and Gabor feature extractors. These features dimension is huge while combining the features of canny edge detector and Gabor extractor. The Principal Component Analysis applied on the extracted features to reduce the dimension to increase the computational speed. The ensemble method Random Forest is applied on the features to classify the vessel's existence in fundus image. The results have been compared with Decision Tree algorithm. The experiments have proved the Random Forest performed better result with 99.86% accuracy and F1 score 0.997.",
keywords = "blood vessel segmentation, canny edge, decision tree, Gabor filter, random forest, retinal vessels",
author = "P. Kuppusamy and Basha, {Mehfooza Munavar} and Hung, {Che Lun}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022 ; Conference date: 25-03-2022 Through 26-03-2022",
year = "2022",
doi = "10.1109/ICSTSN53084.2022.9761339",
language = "English",
series = "1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022",
address = "美國",
}