Initial Analysis of Multiple Retinal Diseases Classification with Fuzzy Medical Image Retrieval

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254717" target="_blank" >RIV/61989100:27240/23:10254717 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10394609" target="_blank" >https://ieeexplore.ieee.org/document/10394609</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SMC53992.2023.10394609" target="_blank" >10.1109/SMC53992.2023.10394609</a>

Result language
angličtina
Original language name
Initial Analysis of Multiple Retinal Diseases Classification with Fuzzy Medical Image Retrieval
Original language description
Medical image retrieval is a highly discussed topic, and it includes an efficient classification of diagnoses based on the similarity search in large databases of medical images. It is very important for early and correct diagnosis and treatment. In this paper, we focus on detecting four diagnoses of treatable retinal diseases in optical coherence tomography (OCT) images. The fuzzy medical image retrieval model (FMIR) is applied to transfer images to fuzzy signatures organized in Fuzzy S-tree, as it was previously successfully used for breast cancer detection and COVID-19 chest X-ray detection. The paper examines and compares the performance of the FMIR method on 4-class and binary classification models built on an OCT dataset and compares the impact of two metrics, Euclidean and Hamming fuzzy distances. The experiments show a clear dominance of Hamming fuzzy distance. The best accuracy is achieved for binary classification (61.16 - 93.8%), while the performance of the 4-class model is worse (51.7%). The distribution of signature space and classification performance are analyzed in detail.
Czech name
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Czech description
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Project
<a href="/en/project/GF22-34873K" target="_blank" >GF22-34873K: Constrained Multiobjective Optimization Based on Problem Landscape Analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 2023
ISBN
979-8-3503-3703-7
ISSN
1062-922X
e-ISSN
2577-1655
Number of pages
6
Pages from-to
4926-4931
Publisher name
IEEE
Place of publication
Piscataway
Event location
Honolulu
Event date
Oct 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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