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Retinal Image Processing and Classification Using Convolutional Neural Networks

  • Karuna Rajan
  • C. SreejithEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

This study aims to develop a system to distinguish retinal disease from fundus images. Precise and programmed analysis of retinal images has been considered as an effective way for the determination of retinal diseases such as diabetic retinopathy, hypertension, arteriosclerosis, etc. In this work, we extracted different retinal features such as blood vessels, optic disc and lesions and then applied convolutional neural network based models for the detection of multiple retinal diseases with fundus photographs involved in structured analysis of the retina (STARE) database. Augmentation techniques like translations and rotations are done for expanding the number of images. The blood vessel extraction is done with the help of morphological operations like dilation and erosion and enhancement operations like CLAHE and AHE. The optic disc is localized by the methods such as opening, closing, Canny’s edge detection and finally thresholding the image after filling the holes. The bright lesions (exudates) inside the retina are detected by the filtering operations and contrast enhancement after the removal of the optic disc. In this study, we experimented with different retinal features as input to convolutional neural networks for effective classification of retinal images.

Keywords

AHE Canny’s edge detection CLAHE Convolutional neural networks Dilation Erosion Lesions Optic nerve Segmentation STARE 

Notes

Acknowledgements

This work was fully funded by Calpine Labs, UVJ Technologies, Kochi, India. We are also immensely grateful to Mr. Bijeesh Devassy, Project Manager, UVJ Technologies and Dr. Asharaf S, Associate Professor, IIITM-K for sharing their pearls of wisdom with us during the course of this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Calpine LabsUVJ Technologies Pvt.LtdKochiIndia

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