Publication
Detection and Mosaicing through Deep Learning Models for Low-Quality Retinal Images
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | pt_PT |
| dc.contributor.advisor | Coelho, Paulo Jorge Simões | |
| dc.contributor.advisor | Cunha, António Manuel Trigueiros da Silva | |
| dc.contributor.author | Correia, Tales Veríssimo Souza | |
| dc.date.accessioned | 2023-11-06T14:49:37Z | |
| dc.date.available | 2023-11-06T14:49:37Z | |
| dc.date.issued | 2023-06-16 | |
| dc.description.abstract | Glaucoma is a severe eye disease that is asymptomatic in the initial stages and can lead to blindness, due to its degenerative characteristic. There isn’t any available cure for it, and it is the second most common cause of blindness in the world. Most of the people affected by it only discovers the disease when it is already too late. Regular visits to the ophthalmologist are the best way to prevent or contain it, with a precise diagnosis performed with professional equipment. From another perspective, for some individuals or populations, this task can be difficult to accomplish, due to several restrictions, such as low incoming resources, geographical adversities, and travelling restrictions (distance, lack of means of transportation, etc.). Also, logistically, due to its dimensions, relocating the professional equipment can be expensive, thus becoming not viable to bring them to remote areas. In the market, low-cost products like the D-Eye lens offer an alternative to meet this need. The D-Eye lens can be attached to a smartphone to capture fundus images, but it presents a major drawback in terms of lower-quality imaging when compared to professional equipment. This work presents and evaluates methods for eye reading with D-Eye recordings. This involves exposing the retina in two steps: object detection and summarization via object mosaicing. Deep learning methods, such as the YOLO family architecture, were used for retina registration as an object detector. The summarization methods presented and inferred in this work mosaiced the best retina images together to produce a more detailed resultant image. After selecting the best workflow from these methods, a final inference was performed and visually evaluated, the results were not rich enough to serve as a pre-screening medical assessment, determining that improvements in the actual algorithm and technology are needed to retrieve better imaging. | pt_PT |
| dc.identifier.tid | 203379560 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.8/8892 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Glaucoma | pt_PT |
| dc.subject | Deep Learning | pt_PT |
| dc.subject | Mosaicing | pt_PT |
| dc.title | Detection and Mosaicing through Deep Learning Models for Low-Quality Retinal Images | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia Electrotécnica - Energia e Automação | pt_PT |
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