Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma
Glaucoma refers to ocular disorders that are characterized by damage to the optic nerve or loss in the field of vision. It is often associated with an increased intraocular pressure of the eye. Continuous damage to the optic nerve may lead to permanent loss of vision. Angle closure glaucoma, one of...
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sg-ntu-dr.10356-652412023-03-03T20:47:16Z Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma Kaushik, Rinsha Lin Weisi School of Computer Engineering DRNTU::Engineering::Electrical and electronic engineering Glaucoma refers to ocular disorders that are characterized by damage to the optic nerve or loss in the field of vision. It is often associated with an increased intraocular pressure of the eye. Continuous damage to the optic nerve may lead to permanent loss of vision. Angle closure glaucoma, one of the categories of glaucoma, can happen suddenly and leads to an emergency situation. It can occur due to different underlying mechanisms and sometimes due to a combination of two or more of them. Usually, Laser Peripheral Iridotomy (LPI) is used to treat angle closure glaucoma. However, it may not be effective because of its inability to address all the underlying mechanisms. Therefore, it becomes vital to identify the combinatorial mechanisms underlying angle closure glaucoma. In this project, a novel approach has been taken to obtain the optimal set of minimum features which aid in the accurate detection of angle closure glaucoma caused due to combinatorial mechanisms. Ensemble learning has been used to achieve our goal. This method has been compared with the ‘simplistic ranks fusion’ method proposed for identification of single mechanisms by Niwas and our results have been found to be superior. Bachelor of Engineering (Computer Engineering) 2015-06-17T03:46:56Z 2015-06-17T03:46:56Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/65241 en Nanyang Technological University 68 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Kaushik, Rinsha Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
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Glaucoma refers to ocular disorders that are characterized by damage to the optic nerve or loss in the field of vision. It is often associated with an increased intraocular pressure of the eye. Continuous damage to the optic nerve may lead to permanent loss of vision. Angle closure glaucoma, one of the categories of glaucoma, can happen suddenly and leads to an emergency situation. It can occur due to different underlying mechanisms and sometimes due to a combination of two or more of them. Usually, Laser Peripheral Iridotomy (LPI) is used to treat angle closure glaucoma. However, it may not be effective because of its inability to address all the underlying mechanisms. Therefore, it becomes vital to identify the combinatorial mechanisms underlying angle closure glaucoma. In this project, a novel approach has been taken to obtain the optimal set of minimum features which aid in the accurate detection of angle closure glaucoma caused due to combinatorial mechanisms. Ensemble learning has been used to achieve our goal. This method has been compared with the ‘simplistic ranks fusion’ method proposed for identification of single mechanisms by Niwas and our results have been found to be superior. |
author2 |
Lin Weisi |
author_facet |
Lin Weisi Kaushik, Rinsha |
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Final Year Project |
author |
Kaushik, Rinsha |
author_sort |
Kaushik, Rinsha |
title |
Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
title_short |
Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
title_full |
Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
title_fullStr |
Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
title_full_unstemmed |
Ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
title_sort |
ensemble learning for reliable feature detection in combinatorial mechanism induced angle closure glaucoma |
publishDate |
2015 |
url |
http://hdl.handle.net/10356/65241 |
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1759853866752409600 |