Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment.

Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.

Structural irregularities in MR corpus callosal images and their association with cerebrospinal fluid biomarkers in Mild Cognitive Impairments.

In this study, irregularity measures from MR images of corpus callosal brain structures in healthy and Mild Cognitive Impairment (MCI) conditions are extracted and their association with Cerebrospinal Fluid (CSF) biomarkers are analyzed. For this, MR images of healthy controls, Early MCI (EMCI) and Late MCI (LMCI) subjects are considered from a public database. The considered images are preprocessed and corpus callosal structure is segmented.

Explainable Optimized LightGBM Based Differentiation of Mild Cognitive Impairment Using MR Radiomic Features.

In this study, an explainable Bayesian Optimized (BO) LightGBM model is employed to differentiate the Corpus Callosal (CC) image features of Healthy Controls (HC) and Mild Cognitive Impairment (MCI). For this, Magnetic Resonance (MR) brain images obtained from a public database are pre-processed and CC is segmented using spatial fuzzy clustering-based level set. Radiomic features are extracted from the segmented CC, which are further fed to BO-LightGBM classifier.

Differentiation of Alzheimer Conditions in MR Brain Images Using a Single Inception Module Network.

In this study, an attempt has been made to differentiate Alzheimer's Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network.