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Optical and Quantum Electronics
Erschienen in: Optical and Quantum Electronics 10/2023

01.10.2023

Optical system based data classification for diabetes retinopathy detection using machine language with artificial intelligence

verfasst von: Suraj Malik, S. Srinivasan, Chandra Shekhar Rajora, Sachin Gupta, Mohammed Mujeer Ulla, Neeraj Kaushik

Erschienen in: Optical and Quantum Electronics | Ausgabe 10/2023

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Abstract

Diabetes causes DR. Diabetes duration influences retinopathy development. The retinal vein weakening may have no side effects or a little eyesight impairment at initially. Blindness may occur. DR intervention and treatment need early clinical indications. Thus, frequent eye examinations must guide patients to a doctor for a full eye inspection and therapy to avoid irreversible vision loss. This work develops a machine learning-based optical image-based data classification method for diabetic retinopathy identification. OCT analyses the retinal picture and the ensemble pulse coupled filtering and green histogram channel equalization-based adaptive filtering segment this picture for blood vessel characterization. CenterResnet-50 classifies images for color fundus detection. Classification accuracy, sensitivity, specificity, AUC, and ROC curves were examined for various optical retina pictures. The proposed method has 98% classification accuracy, 67% sensitivity, 73% specificity, and 63% AUC.
Vorheriger Artikel A computational study for optical and nonlinear optical properties of distinctive V-shaped cyclopenta dithiophene derivatives
Nächster Artikel Wide tuning range q-switched ytterbium doped fiber laser based on V2AlC saturable absorber

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Literatur
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Metadaten
Titel
Optical system based data classification for diabetes retinopathy detection using machine language with artificial intelligence
verfasst von
Suraj Malik
S. Srinivasan
Chandra Shekhar Rajora
Sachin Gupta
Mohammed Mujeer Ulla
Neeraj Kaushik
Publikationsdatum
01.10.2023
Verlag
Springer US
Erschienen in
Optical and Quantum Electronics / Ausgabe 10/2023
Print ISSN: 0306-8919
Elektronische ISSN: 1572-817X
DOI
https://doi.org/10.1007/s11082-023-05193-x

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