Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different proces...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/2/19 |
| _version_ | 1797479057008361472 |
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| author | José Camara Alexandre Neto Ivan Miguel Pires María Vanessa Villasana Eftim Zdravevski António Cunha |
| author_facet | José Camara Alexandre Neto Ivan Miguel Pires María Vanessa Villasana Eftim Zdravevski António Cunha |
| author_sort | José Camara |
| collection | DOAJ |
| description | Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients. |
| first_indexed | 2024-03-09T21:40:22Z |
| format | Article |
| id | doaj.art-99e7348591d8415fb7b21650a2d332cb |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T21:40:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-99e7348591d8415fb7b21650a2d332cb2023-11-23T20:33:03ZengMDPI AGJournal of Imaging2313-433X2022-01-01821910.3390/jimaging8020019Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and ClassificationJosé Camara0Alexandre Neto1Ivan Miguel Pires2María Vanessa Villasana3Eftim Zdravevski4António Cunha5R. Escola Politécnica, Universidade Aberta, 1250-100 Lisboa, PortugalInstituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, PortugalEscola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, PortugalCentro Hospitalar Universitário Cova da Beira, 6200-251 Covilhã, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North MacedoniaInstituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, PortugalArtificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.https://www.mdpi.com/2313-433X/8/2/19eye diseasesglaucoma screeningartificial intelligencedeep learningimage processingglaucoma classification |
| spellingShingle | José Camara Alexandre Neto Ivan Miguel Pires María Vanessa Villasana Eftim Zdravevski António Cunha Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification Journal of Imaging eye diseases glaucoma screening artificial intelligence deep learning image processing glaucoma classification |
| title | Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification |
| title_full | Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification |
| title_fullStr | Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification |
| title_full_unstemmed | Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification |
| title_short | Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification |
| title_sort | literature review on artificial intelligence methods for glaucoma screening segmentation and classification |
| topic | eye diseases glaucoma screening artificial intelligence deep learning image processing glaucoma classification |
| url | https://www.mdpi.com/2313-433X/8/2/19 |
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