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Detection of Diabetic Retinopathy on...

Kalmangi, Sonica.

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Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework./
Author:	Kalmangi, Sonica.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	38 p.
Notes:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13858390
ISBN:	9781392238837

Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework.
Kalmangi, Sonica.

Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 38 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.)--University of Massachusetts Boston, 2019.

This item must not be added to any third party search indexes.

Diabetic Retinopathy (DR) is a leading cause of vision loss among people with diabetes and is mainly caused by damages to blood vessels in the retinal region of the eye. However, the good news is that it is preventable in 98% of the patients provided the symptoms are diagnosed at an early stage. What makes diagnosis challenging is that there are about 415 million people in the world with diabetes and over 30% of them are at risk of developing DR related vision complications. The current ophthalmology screening process is both expensive and time consuming, making it infeasible to screen all diabetes patients for DR. A key step in screening involves identifying visible deviations in the retinal images as compared to the images from a healthy individual's eye. There are clinically documented patterns like circular red spots, swelling of tissues, yellow material deposits and unusual blood vessels that are key to identifying occurrence of DR. This thesis presents Deep Learning (DL) models constructed using Google's Inception framework for recognizing these unusual patterns in retinal images. The performance of these models, image preprocessing techniques and the benefits of using Google Cloud Platform (GCP) are also discussed.

ISBN: 9781392238837Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Deep learning

Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 38 p.
500 $a Source: Masters Abstracts International, Volume: 80-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Pomplun, Marc.
502 $a Thesis (M.S.)--University of Massachusetts Boston, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Diabetic Retinopathy (DR) is a leading cause of vision loss among people with diabetes and is mainly caused by damages to blood vessels in the retinal region of the eye. However, the good news is that it is preventable in 98% of the patients provided the symptoms are diagnosed at an early stage. What makes diagnosis challenging is that there are about 415 million people in the world with diabetes and over 30% of them are at risk of developing DR related vision complications. The current ophthalmology screening process is both expensive and time consuming, making it infeasible to screen all diabetes patients for DR. A key step in screening involves identifying visible deviations in the retinal images as compared to the images from a healthy individual's eye. There are clinically documented patterns like circular red spots, swelling of tissues, yellow material deposits and unusual blood vessels that are key to identifying occurrence of DR. This thesis presents Deep Learning (DL) models constructed using Google's Inception framework for recognizing these unusual patterns in retinal images. The performance of these models, image preprocessing techniques and the benefits of using Google Cloud Platform (GCP) are also discussed.
590 $a School code: 1074.
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653 $a Inception
653 $a Machine learning
653 $a Retinal image
653 $a Tensorflow
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690 $a 0984
710 2 $a University of Massachusetts Boston. $b Computer Science (MS). $3 3544341
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