5709185
doi
10.35940/ijitee.A9594.1111121
oai:zenodo.org:5709185
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Publisher
K. Arunabhaskar
Department of Information Technology, Aditya Engineering College, Surampalem (AP), India
Ch. Mani Mala
MBBS, DO, FCO (LVPEI), Senior Consultant & Phaco Surgeon, Vasan Eye Care, Kakinada (AP), India.
Pigeon Metheuristic Optimized Generative Adversarial Networks and ARKFCM Algorithms for retinal Vessel Segmentation and Classification
R. Kiran Kumar
Department of Computer Science & Engineering, Krishna University, Machilipatnam (AP), India.
issn:2278-3075
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Discrete Wavelet Transform (DWT), Adaptive Regularized Kernel Based Fuzzy Clustering Means (ARKFCM), Diabetic Retinopathy (DR), and modified Generative Adversarial Networks (GAN) algorithm.
<p>Automatic evaluation of retinal vessels acts a significant part in diagnosis of several ocular and systemic diseases. Eye diseases must be diagnosed early to avoid severe infection and vision loss. The method of segmentation and classification of the retinal blood vessel identification is most difficult tasks in computerized fundus imaging now a days. To solve this problem in this paper, to locate retinal vessel in the retinal vessel, Adaptive Regularized Kernel Based Fuzzy Clustering Means (ARKFCM) algorithm-based segmentation is used. For retinal vessel prediction purpose in this paper a PIGEON optimization-based learning rate modified Generative Adversarial Networks (GAN) algorithm is introduced. Additionally, to improve the proposed classification performance input image is transformed with the aid of Discrete Wavelet Transform (DWT). The DWT applied Low Low (LL) image and segmented images are cascaded. The cascade images are used for training and testing. The proposed system has validated with the help of DRIVE and STARE publically available datasets. They are studied by applying a Convolutional Neural Network, an instantly trained neural network for predicting retinal vessel. In the end, the system is checked for system efficiency using the results of modeling based on MATLAB. The scheme guarantees an accuracy of 92.77% on DRIVE dataset and 98.85% on STARE dataset with a minimum average classification error of 2.57%. Further, we recommended to physician for implement the real time clinical application; this scheme is highly beneficial for doctors for identifying retinal blood vessels</p>
Zenodo
2021-11-30
info:eu-repo/semantics/article
5709184
1637243328.993486
452836
md5:fcbff787db2a4bc0cf7b306711ac7409
https://zenodo.org/records/5709185/files/A95941111121.pdf
public
2278-3075
Is cited by
issn
International Journal of Innovative Technology and Exploring Engineering (IJITEE)
11
1
28-34
2021-11-30