The most popular practice in the field of ophthalmology is to diagnose diabetic eye diseases like Diabetic Retinopathy (DR), Macular Edema (ME), Glaucoma or Age-Related Macular Degeneration (AMD) by analyzing retinography. Retinography is an imaging method used to examine the fundus of the eye and assess the severity of the disease. Manual analysis to examine and evaluate the images by trained ophthalmologists is the ultimate practice for identifying abnormalities in retinal visual impairment. However, it is time-consuming and needs expertise. Automated computer-aided diagnostic (CAD) tools with better image processing capability plays a better role to segment blood vessels and find out abnormal patterns caused by the disease. Artificial intelligence (AI)-based models develop best representation for the prediction of the disease in a self-learning technique by automatically extracting features from raw fundus images. In spite of their wide use, there is a lack of professional liability that prevents the model acceptance. It is extremely important to unfold the black box nature of the models to have a clear and detailed explanation about their learning behavior and the factors facilitating certain predictions. Here, we have written a review paper to survey the existing explainable CAD system for the early detection of diabetic eye diseases. Our aim of this paper is to identify the pros and cons of explainable AI-based CAD system to help the research community and the ophthalmologists to select intended models for clinical usage.


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    Titel :

    Explanable CAD System for Early Detection of Diabetic Eye Diseases: A Review


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Das, Pallabi (Autor:in) / Nayak, Rajashree (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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