Iris imaging for health diagnostics
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119548 |
| _version_ | 1811075576138563584 |
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| author | Yu, Tania Weidan |
| author2 | Richard Fletcher. |
| author_facet | Richard Fletcher. Yu, Tania Weidan |
| author_sort | Yu, Tania Weidan |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T10:08:38Z |
| format | Thesis |
| id | mit-1721.1/119548 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:08:38Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195482019-04-10T09:03:34Z Iris imaging for health diagnostics Yu, Tania Weidan Richard Fletcher. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 49-51). The development of mobile technology and machine learning tools has made it easier than ever to monitor health without visiting a doctor. In this thesis, we explore the use of iris imaging as a medical diagnostic tool. We implement a system in which images captured using a mobile device can be uploaded to and analyzed by a central server. With this platform, we hope to build a large database of standard iris images with labeled medical data and facilitate studies of iris diagnostics. In our implementation, the feature extraction and classification tools built are applied to predict diabetes, through a study conducted in collaboration with researchers at Swami Vivekananda Yoga Anusandhana Samsthana (SVYASA). The results show improvement in prediction accuracy and encourage further development of the server platform for future, large-scale studies. by Tania Weidan Yu. M. Eng. 2018-12-11T20:39:40Z 2018-12-11T20:39:40Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119548 1076272965 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Yu, Tania Weidan Iris imaging for health diagnostics |
| title | Iris imaging for health diagnostics |
| title_full | Iris imaging for health diagnostics |
| title_fullStr | Iris imaging for health diagnostics |
| title_full_unstemmed | Iris imaging for health diagnostics |
| title_short | Iris imaging for health diagnostics |
| title_sort | iris imaging for health diagnostics |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119548 |
| work_keys_str_mv | AT yutaniaweidan irisimagingforhealthdiagnostics |