TY  - BOOK
AU  - Karuna Rajan (91616008)
TI  - Retinal image processing and classification using convolutional neural networks
KW  - CONVOLUTIONAL NEURAL NETWORK
KW  - RETINAL IMAGE PROCESSING
KW  - DEEP LEARNING
N1  - In the field of medical image processing, deep learning approaches is emerging out as powerful
tool for analysis and disease detection. The project aims to develop a system to distinguish
retinal diseases from fundus images. Precise and programmed analysis of retinal images has been
considered as an effective tool for the determination of retinal diseases such as diabetic
retinopathy, hypertension, arteriosclerosis, etc. In this study, we extracted different retinal
features such as blood vessels, optic disc, lesions, etc., and applied Convolutional Neural
network based models to detect various disease from fundus images involved in the STructured
Analysis of REtina. Augmentation techniques like translations and rotations were done for
increasing the number of images. The blood vessel extraction was done with the help of
morphological operations like dilation and erosion, and enhancement operations like CLAHE,
AHE. The bright lesions (exudates) inside the retina can be detected by the filtering operations
and contrast enhancement after the removal of the optic disc. The optic disc can be localised by
the methods such as opening, closing, Canny’s edge detection, and finally thresholding the image
after filling the holes. At the end of the stage of classification, we will get the disease classes
with the probabilities. We used the Convolutional Neural Networks for this grouping purpose.
The convolutional model for 10 classes with the RGB images as input, got 42% as the accuracy.
The model with the green channel images are expected to have the highest accuracy. Due to the
small size of datasets, the deep learning techniques in this study were ineffective to be applied
for the detection of the disease.  

ER  -