Retinal image processing and classification using convolutional neural networks (Record no. 6116)

MARC details
000 -LEADER
fixed length control field 02321nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122841.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180605b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Karuna Rajan (91616008)
9 (RLIN) 14312
245 ## - TITLE STATEMENT
Title Retinal image processing and classification using convolutional neural networks
300 ## - PHYSICAL DESCRIPTION
Extent MSC MI 2016-2018
500 ## - GENERAL NOTE
General note In the field of medical image processing, deep learning approaches is emerging out as powerful<br/>tool for analysis and disease detection. The project aims to develop a system to distinguish<br/>retinal diseases from fundus images. Precise and programmed analysis of retinal images has been<br/>considered as an effective tool for the determination of retinal diseases such as diabetic<br/>retinopathy, hypertension, arteriosclerosis, etc. In this study, we extracted different retinal<br/>features such as blood vessels, optic disc, lesions, etc., and applied Convolutional Neural<br/>network based models to detect various disease from fundus images involved in the STructured<br/>Analysis of REtina. Augmentation techniques like translations and rotations were done for<br/>increasing the number of images. The blood vessel extraction was done with the help of<br/>morphological operations like dilation and erosion, and enhancement operations like CLAHE,<br/>AHE. The bright lesions (exudates) inside the retina can be detected by the filtering operations<br/>and contrast enhancement after the removal of the optic disc. The optic disc can be localised by<br/>the methods such as opening, closing, Canny’s edge detection, and finally thresholding the image<br/>after filling the holes. At the end of the stage of classification, we will get the disease classes<br/>with the probabilities. We used the Convolutional Neural Networks for this grouping purpose.<br/>The convolutional model for 10 classes with the RGB images as input, got 42% as the accuracy.<br/>The model with the green channel images are expected to have the highest accuracy. Due to the<br/>small size of datasets, the deep learning techniques in this study were ineffective to be applied<br/>for the detection of the disease. <br/>
502 ## - DISSERTATION NOTE
Degree type MSC MI
Name of granting institution 2016-2018
Year degree granted INT
-- Mr. Pradeep Kumar K
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element CONVOLUTIONAL NEURAL NETWORK
9 (RLIN) 14313
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element RETINAL IMAGE PROCESSING
9 (RLIN) 14314
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DEEP LEARNING
9 (RLIN) 14315
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   05/06/2018   R-1410 05/06/2018 05/06/2018 Project Reports