ItemConference Paper

Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning

Title
Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning
Creator
Lamani, Manjunath Ramanna; Julian Benadit, P.; Guruprasad, C.
Description
In the quest to enhance the diagnostic accuracy of neural disorders, particularly autism, this paper presents a novel approach for brain region classification using advanced machine learning techniques. The study utilizes the ABIDE and AAL116 atlas datasets, focusing on segmenting and classifying brain regions from resting-state functional MRI (rs-fMRI) images. We propose a three-stage process. In the first stage, data collection and preprocessing are conducted, where rs-fMRI images are preprocessed into SPM12-NIfTI format. The second stage involves the segmentation of brain regions using a Regularized VNet, resulting in the extraction of AAL116 brain region images, which are then split into training, testing, and validation sets. In the third stage, we introduce a custom-designed BRCNet (Brain Region Classification Network), which discriminates between Autism and Normal classes. Our segmentation methods are rigorously evaluated using metrics such as Dice Score, Recall, and Precision, with the proposed method achieving a Dice Score of 0.985, Recall of 0.962, and Precision of 0.991, surpassing other tested methods like UNet, Active Contour, and Binary Unit. For classification, various methods, including Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks like ResNet, are compared. Our findings demonstrate that ResNet achieves an exemplary performance with an Accuracy of 97.5%, Sensitivity of 96.2%, Specificity of 97.1%, Precision of 97%, and an F-Measure of 97.93%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1144;pp.373-385
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Autism diagnosis; Brain region classification; BRCNet performance metrics; Machine learning in neuroimaging; Regularized VNet segmentation; Resting-state fMRI
Coverage
Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Guruprasad C., Department of Psychiatry, Bangalore Medical College and Research Institute (BMCRI), Karnataka, Bangalore, 560002, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981977838-6;
Format
online
Language
English
Type
Conference paper

Citation

Lamani, Manjunath Ramanna; Julian Benadit, P.; Guruprasad, C., “Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25656.

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