Integrating Diverse Approaches in Medical Image Analysis: PAA-CNN and Feature Extraction Fusion for Classiation of Psychological Disorders using Anatomical Scans
subject: anatomical images; feature extraction; machine learning; peicewise aggregate approximation; prominent volumes
description: A psychological disorder is a condition that impacts a persons behavior. Due to the contemporary way of life, a large number of individuals suffer from disorders like stress, depression, and other similar ones. These might turn into severe issues that would signiantly impact a persons quality of life. We present a sample framework that uses an Anatomical scan captured along with fMRI. Anatomical scans were used to extract characteristics, which were then utilized to classify using a random forest classir. In a follow-up experiment, CNN is applied to features obtained from the piecewise aggregate approximation method for multi-class classiation of psychological disorders. This method performs noticeably better than the conventional feature extraction techniques, and with this approach, obtained an accuracy of up to 79%. Combining several approaches may boost the classiation and prediction accuracy of medical data. 2025 The Authors. Published by Elsevier B.V.
creator: Nagashruthi, M.K.; Hemanth, K.S.
source: Procedia Computer Science;Volume;260;pp.40-47
publisher: Elsevier B.V.
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 18770509;
language: English
type: Conference paper
coverage: Nagashruthi M.K., School Of Computer Science and Applications, REVA University, Bengaluru, 560064, India; Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, 560073, India