Deep Learning Approaches for Early Detection of Alzheimer's Disease using MRI Neuroimaging

Bhargavi, M S; Prabhakar, Bharani

doi:10.1109/csi54720.2022.9924058

Cited by 3 publications

(1 citation statement)

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“…Early detection is crucial in order to receive appropriate treatment from specialist's Magnetic resonance imaging (MRI) is a non-intrusive approach used to diagnose and observe brain tissue in individuals with Alzheimer's. MRI results of Alzheimer's patients often reveal abnormalities in the cortical and periventricular regions, hippocampal atrophy and amygdala changes in the subcortical region which are early indicators of dementia [6][7][8][9][10].AD has a prolonged preclinical phase, which begins around 10 to 16 years at the least before appearance of noticeable symptoms. The brains of older individuals are examined for clinical diagnosis with no cognitive impairment (CN) or mild cognitive impairment (MCI), similar pathological symptoms to those with confirmed AD are often kept under observation suggesting presence of an asymptomatic phase of AD that varies among the elderly population.…”

Section: Introductionmentioning

confidence: 99%

Enhancing MCI Detection with a Hybrid Machine Learning Approach

Sujathakumari,

Kulkarni,

Chinmaya

et al. 2023

Atlantis Highlights in Computer Sciences

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Mild Cognitive Impairment (MCI) is a prodromal stage of dementia, is often native with very high risk of evolving into Alzheimer's disease. Early detection and accurate classification of MCI can significantly aid in timely intervention and personalized treatment planning. In the study conducted, we put forward a hybrid machine learning approach to enhancing MCI detection using a combination of feature engineering, feature selection, and ensemble learning algorithm. by using standalone Recurrent Neural Networks (RNN) as well as Convolutional Neural Networks (CNN). The proposed method leverages the temporal dependencies captured by RNN and the spatial information extracted by CNN to increase the robustness and accuracy of MCI classification. We used a comprehensive dataset ADNI consisting of neuroimaging and clinical data from a large cohort of subjects, including MCI sufferers and healthy controls. The neuroimaging data encompassed structural MRI scans, while the clinical data encompassed various cognitive assessments. Neuroimaging data is preprocessed to extract relevant features and combine them with the clinical data to create a unified input representation for the hybrid model.

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