EARLY GLAUCOMA DISEASE DETECTION SOFTWARE BASED ON DEEP LEARNING AND FUNDUS IMAGE SEGMENTATION
Glaucoma, often referred to as the "silent thief of sight," causes gradual and asymptomatic vision loss, typically becoming noticeable only after significant peripheral vision has already been lost. To diagnose glaucoma, medical professionals employ various methods, including tonometry...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84866 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Glaucoma, often referred to as the "silent thief of sight," causes gradual and
asymptomatic vision loss, typically becoming noticeable only after significant
peripheral vision has already been lost. To diagnose glaucoma, medical
professionals employ various methods, including tonometry to measure eye
pressure and retinal imaging techniques such as funduscopy. Retinal imaging, a
cornerstone of clinical care for patients with retinal diseases, allows for detailed
observation of the retina's layers and texture. In the context of glaucoma, fundus
images are extensively used for screening to detect damage to the optic nerve head
(ONH) located in front of the retina. Technological advancements play a crucial
role in enhancing medical diagnostics. Notably, the development of artificial
intelligence in the medical field holds significant potential for supporting
diagnostic processes. This research aims to develop a mobile application as an
alternative technology to facilitate early detection and aid in the diagnosis of
glaucoma. The study involves creating and testing an early detection method,
implementing it within mobile software, and ensuring the software is user-friendly
for both the general public and medical professionals. |
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