Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorBuric, Matija
dc.contributor.authorIvasic-Kos, Marina
dc.contributor.authorMartincic-Ipsic, Sanda
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-08-01T18:23:20Z-
dc.date.available2024-08-01T18:23:20Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 377-380.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57412
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectpočítačové viděnícs
dc.subjectvelké jazykové modelycs
dc.subjectsegmentace obrazucs
dc.subjectarchitektura U-Netcs
dc.subjectveterinární oftalmologiecs
dc.subjectlokalizace onemocněnícs
dc.subjectdiagnostické nástrojecs
dc.subjectveterinární diagnostické zobrazovánícs
dc.subjectautomatizovaná diagnózacs
dc.titleThe Disease of the Canine Eye - From Image to Diagnosis Using AIen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis research examines the application of computer vision (CV) and large language models (LLM) in diagnosing eye diseases in dogs. The study utilizes a U-Net framework, incorporating convolutional neural networks (CNNs) such as ResNet, Inception, VGG, and EfficientNet, to enhance the segmentation of eye disease areas. Along the base U-Net model, four U-Net-based models were developed and evaluated on a dataset specifically generated for this purpose, classifying eye diseases into four categories. The performance of the enhanced U-Net architectures was found to be superior to that of the standard U-Net, with the U-Net modified with ResNet34 achieving the best segmentation accuracy, as measured by a Jaccard index of 66.6% on a custom test set. The segmented images were then diagnosed using various LLMs, including ChatGPT, Mistral, Gemini (Bard), Claude, and Llama-2, which were assessed using 15 different symptom sets. The study demonstrates that combining advanced image segmentation techniques with LLMs can improve diagnostic accuracy in veterinary medicine. The approach leverages the segmentation capabilities of U-Net for precise localization and the diagnostic ability of LLMs to interpret symptoms, facilitating enhanced diagnostic tools. This method could be applicable to other medical diagnostic areas requiring similar dual capabilities.en
dc.subject.translatedcomputer visionen
dc.subject.translatedlarge language modelsen
dc.subject.translatedimage segmentationen
dc.subject.translatedU-Net architectureen
dc.subject.translatedveterinary ophthalmologyen
dc.subject.translateddisease localizationen
dc.subject.translateddiagnostic toolsen
dc.subject.translatedveterinary diagnostic imagingen
dc.subject.translatedautomated medical diagnosisen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.41
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
B11-2024.pdfPlný text820,64 kBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/57412

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Full Papers
  5. WSCG 2024: Full Papers Proceedings