An Efficient Deep Learning-Based Classification Framework for Hypertensive Retinopathy / (Record no. 594850)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01868nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1,SAJ |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sajid, Muhammad Zaheer |
| 245 ## - TITLE STATEMENT | |
| Title | An Efficient Deep Learning-Based Classification Framework for Hypertensive Retinopathy / |
| Statement of responsibility, etc. | Muhammad Zaheer Sajid, |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Rawalpindi |
| Name of producer, publisher, distributor, manufacturer | MCS, NUST |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | ix, 45 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Hypertensive retinopathy (HR) is a well-known eye disease that is caused by high blood pressure (hypertension). In this illness, symptoms typically develop later. The AV nicking, cotton wool patches, constricted veins in the optic nerve, and blood pouring into the eye’s optic nerve all contribute to the appearance of the HR symptoms. HR disease may have different types of serious complications, including retinal artery blockage, destruction of the visual nerves, and maybe vision loss. The automated early detection of this illness can be aided by AI and deep learning models. In this research, a novel dataset for HR is collected from Pakistani hospitals (Pak-HR) and internet sources. Second, a brand-new methodology (Incept-HR) is developed to evaluate hypertensive retinopathy using InceptionV3 and residual blocks. 6,000 digital fundus images from the collected datasets were used to train the Incept-HR system. The proposed classification method, Incept-HR, has 99% classification accuracy and an f1-score of 0.99. The results show that this model produces useful outcomes and can be applied as a diagnostic testing tool. The system is not intended to replace optometrists; rather, it aims to assist professionals. The proposed methodology outperforms both the cutting-edge models VGG19 and VGG16 in terms of classification accuracy. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MSCSE / MSSE-27 |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term following geographic name as entry element | MSCSE / MSSE |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Nauman Ali Khan |
| 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 |
|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | Thesis | 05/25/2023 | 005.1,SAJ | MCSTCS-543 | Thesis |