Open Access

ARTICLE

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

Ahmed Zohair Ibrahim^1,*, P. Prakash², V. Sakthivel², P. Prabu³

1 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, P.O. Box 84428, 11671, KSA
2 School of Computer Scence and Engineering, Vellore Institute of Technology, Chennai, 600127, India
3 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India

* Corresponding Author: Ahmed Zohair Ibrahim. Email:

Computer Systems Science and Engineering 2023, 45(3), 2447-2460. https://doi.org/10.32604/csse.2023.030134

Received 18 March 2022; Accepted 25 May 2022; Issue published 21 December 2022

Abstract

In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brainfunction is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer’s disease, Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods.

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Keywords

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