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Detection of Diabetic Retinopathy on...
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Kalmangi, Sonica.
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Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Detection of Diabetic Retinopathy on Google Cloud Platform Using the Inception Framework./
作者:
Kalmangi, Sonica.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
38 p.
附註:
Source: Masters Abstracts International, Volume: 80-12.
Contained By:
Masters Abstracts International80-12.
標題:
Medical imaging. -
電子資源:
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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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.
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