An SVM-based method for classifying retinal lesion vessels

Yifan Zhang; Runan Zheng; Xudong Hu; Chaohong Li; Feng Wang

doi:10.1117/12.2674767

2 May 2023 An SVM-based method for classifying retinal lesion vessels

Yifan Zhang, Runan Zheng, Xudong Hu, Chaohong Li, Feng Wang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422U (2023) https://doi.org/10.1117/12.2674767
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

For higher accuracy of color image registration, this study proposes an SVM-based fast retinal lesion vascular classification method. Firstly, the retinal image is pre-processed by denoising and contrast enhancement, and then the LBP feature vector of vessels is extracted to reduce the dimensionality. Finally, we input the feature vectors into the SVM classifier, choosing the RBF kernel function and defining suitable penalty factors for training. The algorithm performs well in terms of classification with limited small sample labeled data, as demonstrated by experiments, and can recognize retinal vascular occlusion with a recognition rate of 85.29%. In addition, we can make different pre-processing processes for different vessel conditions, which can effectively help improve the fusion accuracy of retinal color images and have practical clinical value.

Citation Download Citation

Yifan Zhang, Runan Zheng, Xudong Hu, Chaohong Li, and Feng Wang "An SVM-based method for classifying retinal lesion vessels", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422U (2 May 2023); https://doi.org/10.1117/12.2674767

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