Browsing by Author "Aditi Bhattacharya"

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A Framework to Diagnose Autism Spectrum Disorder Using Morphological Connectivity of sMRI and XGBoost
(2023) Vaibhavi Gupta; Gokul Manoj; Aditi Bhattacharya; Sandeep Singh Sengar; Rakesh Mishra; Bhoomika R. Kar; Chhitij Srivastava; Jac Fredo Agastinose Ronickom
In this study, we automated the diagnostic procedure of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region, and the morphological connectivity was computed using Pearson correlation. These morphological connections were fed to XGBoost for feature reduction and to build the diagnostic model. The results showed an average accuracy of 94.16% for the top 18 features. The frontal and limbic regions contributed more features to the classification model. Our proposed method is thus effective for the classification of ASD and can also be useful for the screening of other similar neurological disorders.
Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms
(Inderscience Publishers, 2023) Aditi Bhattacharya; Gokul Manoj; Vaibhavi Gupta; Abdul Aleem Shaik Gadda; Dhanvi Vedantham; A. Amalin Prince; Priya Rani; Anandh Kilpattu Ramaniharan; Jac Fredo Agastinose Ronickom
In this study, we compared the performance of geometrical, Zernike moments, and volumetric features of the corpus callosum (CC) to diagnose autism spectrum disorder (ASD). Initially, the CC was segmented from the midsagittal view of 2D structural magnetic resonance images using the distance regularised level set evolution (DRLSE). The segmented images were validated with the ground truth using similarity measures. The geometrical and Zernike moments were extracted from the 2D segmented region, and the volumetric features were extracted from 3D images of CC. The features extracted were then used to train classifiers. The segmented images were highly matched with the ground truth with mean similarity measure values of Sokal and Sneath-II = 0.9928 and Pearson and Heron-II = 0.9924. We achieved the highest site-specific classification accuracy of 72.69% using the random forest (RF) classifier. The pipeline followed in this study can be used for mass screening of ASD-like neurodevelopmental disorders. Copyright © 2023 Inderscience Enterprises Ltd.