Advanced MRI-Based Alzheimer’s Disease Classification with Hybrid Convolutional Neural Networks

Swapna Saturi; Rafia Adiba; I. Yeshwanth Reddy; Md.Arsalan Shareef; D.Prathyush

doi:10.69996/jcai.2025003

Authors

Swapna Saturi Assistant Professor, Department of CSE, Hasanparthy, Hanamkonda, Koukonda, Telangana-506015, India.
Rafia Adiba Student, Department of CSE, Hasanparthy, Hanamkonda, Koukonda, Telangana-506015, India.
I. Yeshwanth Reddy Student, Department of CSE, Hasanparthy, Hanamkonda, Koukonda, Telangana-506015, India
Md.Arsalan Shareef Student, Department of CSE, Hasanparthy, Hanamkonda, Koukonda, Telangana-506015, India
D.Prathyush Student, Department of CSE, Hasanparthy, Hanamkonda, Koukonda, Telangana-506015, India

DOI:

https://doi.org/10.69996/jcai.2025003

Keywords:

Alzheimer’s Disease, Random Forest (RF), Convolutional Neural Networks (CNNs), Magnetic Resonance Imaging (MRI), Support Vector Machines (SVM)

Abstract

The key to effective therapy and management of Alzheimer's disease (AD), a progressive neurological condition, is obtaining a prompt and precise diagnosis. Support Vector Machines (SVMs), Random Forest (RFs), and Gradient Boosting Machines (GBMs) are some of the most used traditional machine learning algorithms for Alzheimer's disease (AD) classification, but they struggle to deal with the complexity of medical imaging data. On the other hand, Convolutional Neural Networks (CNNs) are great at image classification and have shown impressive results in analyzing MRI scans for AD detection. Plus, combining CNNs with traditional machine learning techniques can enhance both accuracy and reliability. This study explores hybrid methodologies that integrate Convolutional Neural Networks (CNNs) with conventional machine learning techniques, such as the combination of CNN with Support Vector Machines (SVM), CNN with Gradient Boosting Machines (GBM), and CNN with Random Forest (RF).The study achieved notable classification performance, with the CNN-SVM model reaching an accuracy of 97% and the CNN-RF model achieving 83%. These results indicate that blending deep learning with conventional machine learning methods can enhance the ability of Alzheimer’s disease (AD) diagnosis, promoting more reliable early detection and supporting the creation of effective intervention strategies

References

[1] S. Alqahtani, A. Alqahtani, M. A. Zohdy, A. A., Alsulami and S. Ganesan, “Severity Grading and Early Detection of Alzheimer’s Disease through Transfer Learning,” Information, vol.14, no.12, pp. 646, 2023.

[2] F. Ambily and I.A. Pandian, “Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network,” Multimedia Tools and Applications, vol.80, no.19, pp. 29585-29600, 2021.

[3] D.A. Arafa, H.E. Moustafa, A. Ali-Eldin, and H.A. Ali, “Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey,” Multimedia Tools and Applications, vol.81, no.17, pp. 23735-23776, 2022.

[4] S.S. Verma, A.D.P. Arya, T. Chakrabarti, A.A. Elngar, A. Kamali and M. Nami, “A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease,” Brain Informatics, vol.10, no.1, pp. 17, 2023.

[5] S.S. Bamber and T. Vishvakarma, “Medical image classification for Alzheimer’s using a deep learning approach,” Journal of Engineering and Applied Science, vol.70, no.1, pp. 54,2023.

[6] A. El-Assy, H.M. Amer, H.M. Ibrahim and M.A. Mohamed, “A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data,” Scientific Reports (Nature Publisher Group), vol.14, no.1, pp. 3463, 2024.

[7] S. Fouladi, A.A. Safaei, N.I. Arshad, M.J. Ebadi and A. Ahmadian, “The use of artificial neural networks to diagnose Alzheimer’s disease from brain images,” Multimedia Tools and Applications, vol.81, no.26, pp. 37681-37721, 2022.

[8] A. Francisand I. Alex Pandian, “Ensemble learning approach for multi-class classification of Alzheimer’s stages using magnetic resonance imaging,” Telkomnika, vol.21, no.2, pp. 374-381, 2023.

[9] F. Hajamohideen, N. Shaffi, M. Mahmud, K. Subramanian, A. Al Sariri et al., “Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function,” Brain Informatics, vol.10, no.1, pp. 5, 2023.

[10]R. Ibrahim, R. Ghnemat and Qasem Abu Al-Haija, “Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization,” AI, vol.4, no.3, pp. 551, 2023.

[11]A. Irshad, H.S. Muhammad, F.A. Sultan and A.A. Ziyad, “Improving Alzheimer’s Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA,” Healthcare, vol.11, no.18, pp.2551, 2023.

[12]W.N. Ismail, P.P. Fathimathul Rajeena and M.A.S. Ali, “MULTforAD: Multimodal MRI Neuroimaging for Alzheimer’s Disease Detection Based on a 3D Convolution Model,” Electronics, vol.11, no.23, pp. 3893, 2022.

[13]M. Odusami, R. Maskeliunas and R. Damaseviˇ Cius, “Pixel-Level Fusion Approachˇ with Vision Transformer for Early Detection of Alzheimer’s Disease,” Electronics, vol.12, no.5, pp. 1218, 2023.

[14]V. Patil, M. Madgi and A. Kiran, “Early prediction of Alzheimer’s disease using convolutional neural network: a review,” The Egyptian Journal of Neurology, Psychiatry, and Neurosurgery, vol.58, no.1, 2022.

[15]B. Selvaganesh and R. Ganesan, “A hybrid segmentation and classification techniques for detecting the neurodegenerative disorder from brain Magnetic Resonance Images,” Multimedia Tools and Applications, vol.81, no.20, pp. 28801-28822, 2022.

[16]A. Bhadrashetty and S. Kulkarni, “Detection and Classification of Alzheimer’s Disease by Employing CNN,” International Journal of Intelligent Systems and Applications, vol.15, no.2, pp. 14, 2023.

[17]N.F. Y, I. Wijayanto, N.K.C. Pratiwi, F.F. Taliningsih, S. Rizal and M.A. Pramudito, “Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture,” Journal of Physics: Conference Series, vol.1844, no.1, 2021.

[18]J. Zamani, A. Sadr and Amir-Homayoun Javadi, “Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer’s disease neuroimaging initiative,” PLoS One, vol.17, no.6, 2022.

[19]Z. Zhao, J.H. Chuah, K.W. Lai, C. Chow, M. Gochoo et al., “Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: A review,” Frontiers in Computational Neuroscience, 2023.

[20]A.A. Abd El-Latif, S.A. Chelloug, M. Alabdulhafith, and M. Hammad, “Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data,” Diagnostics, vol.13, no.7, pp. 1216, 2023.

[21]O. Altwijri, R. Alanazi, A. Aleid, K. Alhussaini, Z. Aloqalaa, M. Almijalli, and A. Saad, “Novel DeepLearning Approach for Automatic Diagnosis of Alzheimer’s Disease from MRI,” Applied Sciences, vol.13, no.24, pp. 13051, 2023.

[22]M.P. Basavaraj, M.R. Raigonda, S. Anakal, and A. Bhadrashetty, “Early Detection of Dementia using Deep Learning and Image Processing,” International Journal of Engineering and Manufacturing, vol.13, no.1, pp. 14, 2023.

[23]V.S. Gaikwad, S. Deore, G.M. Poddar, R.V. Patil, D.S. Hirolikar et al., “Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis,” International Journal of Intelligent Systems and Applications in Engineering, vol.12, no.14s, pp.388–397,

2024.

[24]S.K. Swarnkar and T.A. Tran, “A Survey on Enhancement and Restoration of Underwater Image,” Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques, pp. 1–15, 2023.

[25]Suman Kumar Swarnkar, “Improved Convolutional Neural Network based Sign Language Recognition,” International Journal of Advanced Science and Technology, 2019.

[26]S. Agarwal, J.P. Patra, and S.K. Swarnkar, “Convolutional Neural Network Architecture Based Automatic Face Mask Detection,” International Journal of Health Sciences, no. SPECIAL ISSUE III, pp. 623-629, 2022.

[27]S.K. Swarnkar, G.S. Chhabra, A. Guru, B. Janghel, P.K. Tamrakar and U. Sinha, “Underwater Image Enhancement Using D-Cnn,” NeuroQuantology, vol.20, no.11, pp.2157–2163, 2022.

[28]S.K. Swarnkar, A. Guru, G.S. Chhabra, P.K. Tamrakar, B. Janghel, and U. Sinha, “Deep learning techniques for medical image segmentation classification,” International Journal of Health Sciences, vol.6, no.S10, pp. 408–421, 2022.