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Feature-Based Classification of Mild Cognitive Impairment and Alzheimer’s Disease Based on Optical Coherence Tomographic Angiographic Image
Author(s)
Maneerat, Areerat
Trinavarat, Adisak
Rattanabannakit, Chatchawan
Morkphrom, Ekkaphop
Senanarong, Vorapun
Srinonprasert, Varalak
Songsaeng, Dittapong
Atchaneeyasakul, La Ongsri
Date Issued
August 1, 2024
Type
Article
Abstract
Alzheimer’s disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer’s disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer’s diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer’s disease.
Citation
Sensors, 24(16), 2024