Hippocampus Segmentation-Based Alzheimer’s Disease Diagnosis and Classification of MRI Images

Alzheimer’s disease represents a neurological condition characterized by steady cognitive decline and eventual memory loss due to the death of brain cells. It is one of the most prominent dementia types observed in patients and which hence underlines the imminent need for potential methods to diagno...

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Vydané v:Arabian journal for science and engineering Ročník 48; číslo 8; s. 10249 - 10265
Hlavní autori: Balasundaram, A., Srinivasan, Sruthi, Prasad, A., Malik, Jahan, Kumar, Ayush
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Predmet:
Algorithms
Alzheimer's disease
Brain
computer Engineering and Computer Science
Datasets
Deep learning
Diagnosis
Engineering
Hippocampus
Humanities and Social Sciences
Image classification
Image segmentation
Machine learning
Magnetic resonance imaging
Medical imaging
multidisciplinary
Research Article-computer Engineering and Computer Science
Science
Hippocampus segmentation
Deep learning
Medical image processing
Computer vision
Alzheimer's
ISSN:2193-567X, 1319-8025, 2191-4281
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Shrnutí:Alzheimer’s disease represents a neurological condition characterized by steady cognitive decline and eventual memory loss due to the death of brain cells. It is one of the most prominent dementia types observed in patients and which hence underlines the imminent need for potential methods to diagnose the disease early on. This work considers a novel approach by utilizing a reduced version of one of the datasets used in this work to achieve a considerably accurate prediction while also enabling quicker training. It leverages image segmentation to isolate the hippocampus region from brain MRI images and then strikes a comparison between models trained on the segmented portions and models trained on complete images. This research uses two datasets—4 classes of images from Kaggle and a popular OASIS 2 MRI and demographic dataset. A deep learning-based approach was adopted to train the Kaggle dataset to perform severity classification, and the hippocampus region segmented from a reduced version of the OASIS dataset was trained on supervised and ensemble learning algorithms to detect Alzheimer’s disease. The metric used for the assessment of model performance is classification accuracy. A comparative analysis between the proposed approach and existing work was also performed, and it was observed that the proposed approach is effective in the early diagnosis of Alzheimer’s disease.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-022-07538-2