| 000 -LEADER |
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| fixed length control field |
02611nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Batool, Summiya |
| 245 ## - TITLE STATEMENT |
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| Title |
Deploying Efficient Net (BNs) for grading Diabetic Retinopathy severity levels from fundus images / |
| Statement of responsibility, etc. |
Summiya Batool |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Islamabad : |
| Name of producer, publisher, distributor, manufacturer |
SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
40p. |
| Other physical details |
Soft Copy |
| Dimensions |
30cm |
| 500 ## - GENERAL NOTE |
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| General note |
One of the common and escalating endocrine illnesses is diabetes mellitus. Diabetic<br/>retinopathy is a common eye problem in patients with diabetes. Diabetic retinopathy<br/>(DR), a retinal condition, is acknowledged as an epidemic on a global scale. One-third of<br/>the estimated 285 million persons with diabetes show symptoms of DR, and one-third of<br/>them have DR that threatens their vision [1]. In addition, the figures are rising. By 2040,<br/>288 million individuals are expected to have AMD, and by 2050, the number of people<br/>with DR is projected to treble. The need for reliable diabetic retinopathy screening<br/>systems became a critical issue recently due to the increase in the number of diabetic<br/>patients. The severity of DR can be graded into five stages: normal, mild NPDR, moderate<br/>NPDR, severe NPDR, and PDR. Early diagnosis and treatment of DR can be<br/>accomplished by organizing large regular screening programs. Numerous Convolutional<br/>neural network models are developed for the diagnosis of DR in fundus images using<br/>deep learning methods. In Deep Learning (DL) one of the methods is a computer-aided<br/>medical diagnosis for the detection of DR. There are many DL-based methods such as<br/>restricted Boltzmann Machines, convolutional neural networks (CNNs), auto-encoder,<br/>and sparse coding. On the other hand, it is thought-provoking to distinguish it initially not<br/>display signs in the initial classes. The current models for diabetic retinopathy mayn’t<br/>identify entire classes of DR. The utmost commonly used metrics like accuracy, f1-score,<br/>precision, and recall; do not cogitate the standard of difference among labels, which<br/>supports spotting all classes of DR. In our paper used Efficient Net BNs models. We<br/>concluded evaluation scores using the F1-score, which is applicable for grading various<br/>classes of DR established on the intensity levels. We have accomplished the F1- score of<br/>0.88 and 0.84 using the simple preprocess, Gaussian smoothing filters, and deploying an<br/>Efficient Net BNs network on DeepDRiD and EYE-PACS datasets. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://10.250.8.41:8080/xmlui/handle/123456789/32446">http://10.250.8.41:8080/xmlui/handle/123456789/32446</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
Thesis |
