Opti-MuDR_CNN: An Enhanced Approach for Classification of Early Onset of Alzheimer’s Disease
Opti-MuDR_CNN: An Enhanced Approach for Classification of Early Onset of Alzheimer’s Disease |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-5 |
||
| Year of Publication : 2024 | ||
| Author : Afiya Parveen Begum, Prabha Selvaraj |
||
| DOI : 10.14445/22315381/IJETT-V72I5P127 | ||
How to Cite?
Afiya Parveen Begum, Prabha Selvaraj, "Opti-MuDR_CNN: An Enhanced Approach for Classification of Early Onset of Alzheimer’s Disease," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 261-274, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P127
Abstract
Alzheimer’s Disease (AD) is commonly found in aged people and adults. This kind of disease is predicted using various hybrid methods, such as a combination of Machine Learning (ML) and Deep Learning (DL) in the medical field. Therefore, image classification problems are considered a significant limitation of the existing research and the current scenario. In Current research, a novel technique - Opti Multi Hidden Deep rooted Convolutional Neural Network has been utilized to classify the early onset of AD. In our work, we have given the segmented images to different architectures like EfficientnetB7, Restnet50, and Opti Multi hidden deep-rooted CNN, where our proposed model Opti Multi hidden Deep rooted CNN model has achieved the best accuracy. The proposed method Opti Multi Hidden Deep rooted Convolutional Neural Network has been compared in terms of certain performance measures like MAP - Mean-Average-Precision, F1- score, recall, precision, and accuracy with the existing methods like ResNet50 and EfficientNetB7 and obtained the best results than existing methods. Eventually, the proposed approach achieved more efficient results compared to the other existing methods. In the future, large real-time datasets will be used with the integration of the proposed system to enhance accuracy and sensitivity.
Keywords
Alzheimer’s Disease (AD), MRI, Convolutional Neural Network (CNN), Dwarf Mongoose Optimization Algorithm (DMOA), Deep Learning (DL).
References
[1] Vo Van Giau et al., “Analysis of 50 Neurodegenerative Genes in Clinically Diagnosed Early-Onset Alzheimer’s Disease,” International Journal of Molecular Sciences, vol. 20, no. 6, pp. 1-15, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[ [2] Farheen Ramzan et al., “A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks,” Journal of Medical Systems, vol. 44, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Janani Venugopalan et al., “Multimodal Deep Learning Models for Early Detection of Alzheimer’s Disease Stage,” Scientific Reports, vol. 11, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Elif Eyigoz et al., “Linguistic Markers Predict Onset of Alzheimer’s Disease,” EClinical Medicine, vol. 28, pp. 1-9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Hany Alashwal et al., “The Application of Unsupervised Clustering Methods to Alzheimer’s Disease,” Frontiers in Computational Neuroscience, vol. 13, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Hansruedi Mathys et al., “Single-Cell Transcriptomic Analysis of Alzheimer’s Disease,” Nature, vol. 570, pp. 332-337, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Muazzam Maqsood et al., “Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans,” Sensors, vol. 19, no. 11, pp. 1-19, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] K.R. Kruthika et al., “Multistage Classifier-Based Approach for Alzheimer’s Disease Prediction and Retrieval,” Informatics in Medicine Unlocked, vol. 14, pp. 34-42, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] María Luisa Barragán Pulido et al., “Alzheimer’s Disease and Automatic Speech Analysis: A Review,” Expert Systems with Applications, vol. 150, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Vo Van Giau et al., “Genetic Analyses of Early-Onset Alzheimer’s Disease Using Next Generation Sequencing,” Scientific Reports, vol. 9, pp. 1-10, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Christiane Reitz, Ekaterina Rogaeva and Gary W. Beecham, “Late-Onset vs Nonmendelian Early-Onset Alzheimer Disease A Distinction Without a Difference?,” Neurology Genetics, vol. 6, no. 5, pp. 1-9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Erik C. B. Johnson et al., “Large-Scale Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation,” Nature Medicine, vol. 26, pp. 769-780, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Iris E. Jansen et al., “Genome-Wide Meta-Analysis Identifies New Loci and Functional Pathways Influencing Alzheimer’s Disease Risk,” Nature Genetics, vol. 51, pp. 404-413, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ruhul Amin Hazarika, Debdatta Kandar, and Arnab Kumar Maji, “An Experimental Analysis of Different Deep Learning Based Models for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images,” Journal of King Saud University- Computer and Information Sciences, vol. 34, no. 10, pp. 8576-8598, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Nagaraj Yamanakkanavar, Jae Young Choi, and Bumshik Lee, “MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey,” Sensors, vol. 20, no. 11, pp. 1-28, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] P.M. Rossini et al., “Early Diagnosis of Alzheimer’s Disease: The Role of Biomarkers Including Advanced EEG Signal Analysis. Report from the IFCN-Sponsored Panel of Experts,” Clinical Neurophysiology, vol. 131, no. 6, pp. 1287-1310, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] U. Rajendra Acharya et al., “Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques,” Journal of Medical Systems, vol. 43, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Atif Mehmood et al., “A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease,” Brain Sciences, vol. 10, no. 2, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Victor T.T. Chan et al., “Spectral-Domain OCT Measurements in Alzheimer’s Disease: A Systematic Review and Meta-Analysis,” Ophthalmology, vol. 126, no. 4, pp. 497-510, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Daniel Ferreira, Agneta Nordberg, and Eric Westman, “Biological Subtypes of Alzheimer Disease A Systematic Review and Meta-Analysis,” Neurology, vol. 94, no. 10, pp. 436-448, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ruoxuan Cui, Manhua Liu, and The Alzheimer’s Disease Neuroimaging Initiative, “RNN-Based Longitudinal Analysis for Diagnosis of Alzheimer’s Disease,” Computerized Medical Imaging and Graphics, vol. 73, pp. 1-10, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] D. Chitradevi, and S. Prabha, “Analysis of Brain Sub Regions Using Optimization Techniques and Deep Learning Method in Alzheimer Disease,” Applied Soft Computing, vol. 86, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hao Xiaoke et al., “Multi-Modal Neuroimaging Feature Selection with Consistent Metric Constraint for Diagnosis of Alzheimer’s Disease,” Medical Image Analysis, vol. 60, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] M. Raza et al., “Diagnosis and Monitoring of Alzheimer’s Patients Using Classical and Deep Learning Techniques,” Expert Systems with Applications, vol. 136, pp. 353-364, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Céline Bellenguez, Benjamin Grenier-Boley, and Jean-Charles Lambert, “Genetics of Alzheimer’s Disease: Where We are, and Where We are Going,” Current Opinion in Neurobiology, vol. 61, pp. 40-48, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Jun Pyo Kim et al., “Machine Learning Based Hierarchical Classification of Frontotemporal Dementia and Alzheimer’s Disease,” NeuroImage: Clinical, vol. 23, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Marc Suárez-Calvet et al., “Early Increase of CSF sTREM2 in Alzheimer’s Disease is Associated with Tau Related-Neurodegeneration but Not with Amyloid-β Pathology,” Molecular Neurodegeneration, vol. 14, pp. 1-14, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Su Yang et al., “M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer’s Disease: A Review From the ML Perspective,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 10, pp. 2924-2935, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Dominique Campion, Camille Charbonnier, and Gaël Nicolas, “SORL1 Genetic Variants and Alzheimer Disease Risk: A Literature Review and Meta-Analysis of Sequencing Data,” Acta Neuropathologica, vol. 138, pp. 173-186, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Nour M. Moussa-Pacha et al., “BACE1 Inhibitors: Current Status and Future Directions in Treating Alzheimer’s Disease,” Medicinal Research Reviews, vol. 40, no. 1, pp. 339-384, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Adrien Payan, and Giovanni Montana, “Predicting Alzheimer’s Disease: A Neuroimaging Study with 3D Convolutional Neural Networks,” Arxiv, pp. 1-9, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Chuanchuan Zheng et al., “Automated Identification of Dementia Using Medical Imaging: A Survey from a Pattern Classification Perspective,” Brain Informatics, vol. 3, pp. 17-27, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Emina Alickovic, Abdulhamit Subasi, and for the Alzheimer’s Disease Neuroimaging Initiative, “Automatic Detection of Alzheimer Disease Based on Histogram and Random Forest,” Proceedings of the International Conference on Medical and Biological Engineering, Banja Luka, Bosnia and Herzegovina, vol. 73, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Dinggang Shen et al., Machine Learning Techniques for AD/MCI Diagnosis and Prognosis, Machine Learning in Healthcare Informatics, Springer, Berlin, Heidelberg, vol. 56, pp. 147-179, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Igor O. Korolev et al., “Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia Using Clinical, MRI, and Plasma Biomarkers Via Probabilistic Pattern Classification,” Plos One, vol. 11, no. 2, pp. 1-25, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Fan Li, Manhua Liu, and Alzheimer’s Disease Neuroimaging Initiative, “Alzheimer’s Disease Diagnosis Based on Multiple Cluster Dense Convolutional Networks,” Computerized Medical Imaging and Graphics, vol. 70, pp. 101-110, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Min Jeong Wang et al., “Analysis of Cerebrospinal Fluid and [11C] PIB PET Biomarkers for Alzheimer’s Disease with Updated Protocols,” Journal of Alzheimer’s Disease, vol. 52, no. 4, pp. 1403-1413, 2016.
[CrossRef] [Google Scholar] [Publisher Link]