Ocular Disease Intelligent Recognition / (Record no. 608903)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01543nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sahar, Syeda Ghina |
| 245 ## - TITLE STATEMENT | |
| Title | Ocular Disease Intelligent Recognition / |
| Statement of responsibility, etc. | Syeda Ghina Sahar |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 46p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | To record anatomical details of the eye and anomalies, fundus imaging has proved very<br/>efficient. The most effective way to see and diagnose a wide range of eye diseases is through<br/>fundus imaging. Conditions that affect the blood vessels and areas surrounding it include diabetesrelated retinopathy, glaucoma, AMD, myopia, cataract and hypertension. It's possible for the<br/>patient to have more than one ophthalmological problems that can be seen in one or both of<br/>his eyes. The dataset provided by ODIR is used in this study. The data has eight different categories<br/>for the diseases to be detected. By using transfer learning, two simultaneous models are described<br/>for solving the multi label problem for both the eyes (left and right). For the convolutional network,<br/>two synchronous efficient net models are implemented which are used with ADAM optimizers for<br/>better detection and results outcome. On the ODIR data set, B7 Efficient net along with focal loss<br/>outperformed the other approaches with an accuracy rate of 0.96%. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/30810">http://10.250.8.41:8080/xmlui/handle/123456789/30810</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 04/22/2024 | 610 | SMME-TH-774 | Thesis |