CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
An Efficient Restoration Method for the Faint Dot Matrix Images

Ao Zhu Peng Cao
Random Articles
Reseach Article

Advanced Deep Learning Techniques in Neurological Disorder Imaging a Comprehensive Overview

by Shashikant Upadhyay, Pratima Gautam
journal cover thumbnail

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.

References
  1. M. Xu, Y. Ouyang, and Z. Yuan, "Deep Learning Aided Neuroimaging and Brain Regulation," Sensors (Basel), vol. 23, no. 11, p. 4993, May 2023. doi: 10.3390/s23114993. PMID: 37299724; PMCID: PMC10255716.
  2. A. Saboor, J. P. Li, A. Ul Haq, et al., "DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system," Sci. Rep., vol. 14, p. 6425, 2024. doi: 10.1038/s41598-024-56983-6.
  3. Y. K. Desai, "Diagnosis of medical images using convolutional neural networks," J. Electr. Syst., vol. 20, no. 6s, pp. 2371–2376, May 2024. doi: 10.52783/jes.3220.
  4. H. Zhang and Y. Qie, "Applying Deep Learning to Medical Imaging: A Review," Appl. Sci., vol. 13, p. 10521, 2023. doi: 10.3390/app131810521.
  5. M. M. Taye, "Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions," Computers, vol. 12, p. 91, 2023. doi: 10.3390/computers12050091
  6. A. M. El-Assy, H. M. Amer, H. M. Ibrahim, et al., "A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data," Sci. Rep., vol. 14, p. 3463, 2024. doi: 10.1038/s41598-024-53733-6.
  7. R. Archana and P. S. E. Jeevaraj, "Deep learning models for digital image processing: a review," Artif. Intell. Rev., vol. 57, p. 11, 2024. doi: 10.1007/s10462-023-10631-z.
  8. B. Pandey, D. Kumar Pandey, B. Pratap Mishra, and W. Rhmann, "A comprehensive survey of deep learning in the field of medical imaging and Medical Natural Language Processing: Challenges and Research Directions," J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5083–5099, 2022. doi: 10.1016/j.jksuci.2021.01.007.
  9. M. G. Alsubaie, S. Luo, and K. Shaukat, "Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review," Mach. Learn. Knowl. Extr., vol. 6, pp. 464-505, 2024. doi: 10.3390/make6010024.
  10. A. Cuk, T. Bezdan, L. Jovanovic, et al., "Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics," Sci. Rep., vol. 14, p. 4309, 2024. doi: 10.1038/s41598-024-54680-y.
  11. Y. Badr, U. Tariq, F. Al-Shargie, et al., "A review on evaluating mental stress by deep learning using EEG signals," Neural Comput & Applic., vol. 36, pp. 12629–12654, 2024. doi: 10.1007/s00521-024-09809-5.
  12. R. Wang, V. Bashyam, Z. Yang, et al., "Applications of generative adversarial networks in neuroimaging and Clinical Neuroscience," NeuroImage, vol. 269, p. 119898, 2023. doi: 10.1016/j.neuroimage.2023.119898.
  13. R. Wang, V. Bashyam, Z. Yang, et al., "Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience," ArXiv, 2022. doi: 10.1016/j.neuroimage.2023.119898.
  14. A. Saboor, J. P. Li, A. Ul Haq, et al., "DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system," Sci. Rep., vol. 14, p. 6425, 2024. doi: 10.1038/s41598-024-56983-6.
  15. R. Yousef, S. Khan, G. Gupta, T. Siddiqui, B. M. Albahlal, S. A. Alajlan, M. A. Haq, "U-Net-Based Models towards Optimal MR Brain Image Segmentation," Diagnostics, vol. 13, p. 1624, 2023. doi: 10.3390/diagnostics13091624.
  16. G. Hcini, I. Jdey, and H. Dhahri, "Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review," Neural Process Lett., vol. 56, p. 153, 2024. doi: 10.1007/s11063-024-11600-5.
  17. M. Mahendran and R. Visalakshi, "An ensemble of ResNet model for classification of Parkinson disease," Int. J. Nutr. Pharmacol. Neurol. Dis., vol. 14, no. 1, pp. 9-14, Jan.-Mar. 2024. doi: 10.4103/ijnpnd.ijnpnd_22_23.
  18. S. P. Amit Ganatra, "Advancements in Transfer Learning Strategies for PET and MRI Brain Image Fusion," Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 3, pp. 2037–2044, 2024. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5670.
  19. A. Anaya-Isaza, L. Mera-Jiménez, and M. Zequera-Diaz, "An overview of deep learning in medical imaging," Informatics Med. Unlocked, vol. 26, p. 100723, 2021. doi: 10.1016/j.imu.2021.100723.
  20. K. Armonaite, M. L. Ventura, and L. Laura, "Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective," Explor Neuroprot Ther., vol. 3, pp. 139–150, 2023. doi: 10.37349/ent.2023.00043.
  21. S. Chen and W. Guo, "Auto-Encoders in Deep Learning—A Review with New Perspectives," Mathematics, vol. 11, p. 1777, 2023. doi: 10.3390/math11081777.
  22. M. Xu, Y. Ouyang, and Z. Yuan, "Deep Learning Aided Neuroimaging and Brain Regulation," Sensors, vol. 23, p. 4993, 2023. doi: 10.3390/s23114993.
  23. S. K. Mathivanan, S. Sonaimuthu, and S. Murugesan, et al., "Employing deep learning and transfer learning for accurate brain tumor detection," Sci. Rep., vol. 14, p. 7232, 2024. doi: 10.1038/s41598-024-57970-7.
  24. S. Park, C. No, S. Kim, et al., "A multimodal screening system for elderly neurological diseases based on deep learning," Sci. Rep., vol. 13, p. 21013, 2023. doi: 10.1038/s41598-023-48071-y.
  25. X. Cui, N. Chen, C. Zhao, et al., "An adaptive weighted attention-enhanced deep convolutional neural network for classification of MRI images of Parkinson’s disease," J. Neurosci. Methods, vol. 394, p. 109884, 2023. doi: 10.1016/j.jneumeth.2023.109884.
  26. C. Surianarayanan, J. J. Lawrence, P. R. Chelliah, E. Prakash, and C. Hewage, "Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review," Sensors, vol. 23, p. 3062, 2023. doi: 10.3390/s23063062.
  27. P. Chen, P. Wang, and B. Gao, "The application value of deep learning in the background of precision medicine in glioblastoma," Sci. Prog., vol. 107, no. 1, 2024. doi: 10.1177/00368504231223353.
  28. S. Zhang, J. Yang, Y. Zhang, J. Zhong, W. Hu, C. Li, and J. Jiang, "The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook," Brain Sci., vol. 13, p. 1462, 2023. doi: 10.3390/brainsci13101462.
  29. X. Liu, K. Gao, B. Liu, et al., "Advances in deep learning-based medical image analysis," Health Data Sci., 2021. doi: 10.34133/2021/8786793.
  30. A. Anaya-Isaza, L. Mera-Jiménez, and M. Zequera-Diaz, "An overview of deep learning in medical imaging," Informatics Med. Unlocked, vol. 26, p. 100723, 2021a. doi: 10.1016/j.imu.2021.100723.
  31. M. Irfan, S. Shahrestani, and M. Elkhodr, "Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to alzheimer’s disease time series analysis," Health Care Sci., vol. 3, no. 1, pp. 41–52, 2024. doi: 10.1002/hcs2.84.
  32. R., P., K. B., T., T. V., R., J., R., Nagaraj, T., & N., T. (2024). A hybrid cluster-based intelligent IDS with deep belief network to improve the security over wireless sensor network. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 225–238.
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.

Powered by PhDFocusTM