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Advanced Deep Learning Techniques in Neurological Disorder Imaging a Comprehensive Overview

by Shashikant Upadhyay, Pratima Gautam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Shashikant Upadhyay, Pratima Gautam
10.5120/ijca2024924090

Shashikant Upadhyay, Pratima Gautam . Advanced Deep Learning Techniques in Neurological Disorder Imaging a Comprehensive Overview. International Journal of Computer Applications. 186, 45 ( Oct 2024), 22-31. DOI=10.5120/ijca2024924090

@article{ 10.5120/ijca2024924090,
author = { Shashikant Upadhyay, Pratima Gautam },
title = { Advanced Deep Learning Techniques in Neurological Disorder Imaging a Comprehensive Overview },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 22-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/advanced-deep-learning-techniques-in-neurological-disorder-imaging-a-comprehensive-overview/ },
doi = { 10.5120/ijca2024924090 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.951089+05:30
%A Shashikant Upadhyay
%A Pratima Gautam
%T Advanced Deep Learning Techniques in Neurological Disorder Imaging a Comprehensive Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 22-31
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neurological illnesses, including Parkinson's, Alzheimer's, and brain tumors, are notoriously difficult to detect due to the subtle structural changes in the brain and their complexity. More accurate deep learning algorithms and automated human diagnosis processes are transforming medical image analysis. This research provides a comprehensive review of deep learning methods for using medical imaging to identify neurological diseases. Many models are compared using various performance criteria. CNNs, LSTMs, GANs, U-Net, ResNet, and DenseNet are all accessible. This collection includes measures like recall, specificity, accuracy, and precision, as well as F1 scores and AUC-ROC. The analysis of these models' limits highlights both the benefits and weaknesses of this fast-emerging subject. The findings suggest that deep learning may improve patient outcomes by minimizing unnecessary invasive procedures and enhancing diagnostic accuracy. Remember that there are substantial knowledge gaps in data, model interpretability, and multi-modal data integration. This paper emphasizes the need for using reliable, intelligible, and generally applicable neurological illness models to guide future research and therapy.

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Index Terms

Computer Science
Information Sciences

Keywords

Alzheimer's Disease Brain Tumors Convolutional Neural Networks (CNNs) Deep Learning DenseNet Generative Adversarial Networks (GANs) Long Short-Term Memory Networks (LSTMs) Medical Imaging Neurological Disorders U-Net.

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