Pigeon Metheuristic Optimized Generative Adversarial Networks and ARKFCM Algorithms for retinal Vessel Segmentation and Classification
R. Kiran Kumar1, K. Arunabhaskar2, Ch. Mani Mala3
1Dr. R. Kiran Kumar, Department of Computer Science & Engineering, Krishna University, Machilipatnam (AP), India.
2K. Arunabhaskar*, Department of Information Technology, Aditya Engineering College, Surampalem (AP), India.
3Dr. CH. Mani Mala, MBBS, DO, FCO (LVPEI), Senior Consultant & Phaco Surgeon, Vasan Eye Care, Kakinada (AP), India.
Manuscript received on November 10, 2021. | Revised Manuscript received on November 15, 2021. | Manuscript published on November 30, 2021. | PP: 28-34 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95941111121 | DOI: 10.35940/ijitee.A9594.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Discrete Wavelet Transform (DWT), Adaptive Regularized Kernel Based Fuzzy Clustering Means (ARKFCM), Diabetic Retinopathy (DR), and modified Generative Adversarial Networks (GAN) algorithm.