| Title: | Classification of perimetric data for supporting glaucoma diagnosis |
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| Authors: | ID Belinc, Janja (Author)
ID Zorman, Milan (Mentor) More about this mentor...  |
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| Files: |  MAG_Belinc_Janja_2018.pdf (4,25 MB)
MD5: 7DE0FC9F0DF6E08B24EB1A07D41FB6B5
PID: 20.500.12556/dkum/2afeca8f-031b-48af-8d81-bd5bb5bd404b
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| Language: | English |
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| Work type: | Master's thesis/paper |
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| Typology: | 2.09 - Master's Thesis |
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| Organization: | FZV - Faculty of Health Sciences
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| Abstract: | The aim of the study: Glaucoma is a chronic, progressive and asymptomatic retinal disease which results in an irreversible visual field loss. The main objective of this Master’s thesis work was to study the applicability of classification techniques for supporting glaucoma diagnosis.
Research Methodology: In this study perimetric data was obtained by SPARK strategy implemented in Oculus perimeters and provided by medical experts from the Hospital Universitario de Canarias (HUC). This data was used for constructing the feature vectors for the classification problem. Feature vectors of 66 values and feature vectors of 6 values were tested in the experiments. The proposed classification study attempted to: a) demonstrate that the studied classifiers were able to distinguish between “healthy” and “glaucomatous” eyes using only perimetric data, and b) analyse which feature vector design was the most suitable to accomplish this task.
Results: The classification results showed that classifiers performed better on 6 than on 66 perimetry values, which demonstrated the suitability of the 6 points selected by the SPARK strategy and supported its use in medical field.
Conclusion: In this study two remarkable findings for pattern recognition in perimetric data were obtained. Firstly, that reducing the dataset improved the efficiency of the studied classifier, and secondly, that simple pattern recognition models types were more efficient than complex ones. |
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| Keywords: | Eye disease, visual field, SPARK perimetry, pattern recognition, machine learning, supervised learning, ROC analysis |
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| Place of publishing: | Maribor |
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| Publisher: | [J. Belinc] |
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| Year of publishing: | 2018 |
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| PID: | 20.500.12556/DKUM-70615 |
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| UDC: | 617.7-007.681:004.8(043.2) |
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| COBISS.SI-ID: | 2428580 |
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| NUK URN: | URN:SI:UM:DK:UDAM0HZP |
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| Publication date in DKUM: | 27.08.2018 |
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| Views: | 1517 |
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| Downloads: | 119 |
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| Metadata: |    |
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| Categories: | FZV
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