![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Early Diagnosis of Alzheimer's Disease with Generative Adversarial Networks
Subscribe/Renew Journal
Alzheimer’s disease (AD) early diagnosis plays a pivotal role in effective intervention and patient care. With limited and noisy medical imaging datasets, GANs are utilized to generate synthetic brain images, aiding in the augmentation of existing data. The selected classification algorithms are well-established in computer vision and have demonstrated efficacy in image classification tasks. The GAN is employed for data augmentation, creating synthetic images representative of AD-associated features. Subsequently, the augmented dataset is utilized to train and evaluate the performance of multiple classification algorithms, providing a comprehensive analysis of their effectiveness in AD detection. This research contributes to the field of Alzheimer’s disease diagnosis by integrating GANs for data augmentation and evaluating the performance of ten diverse classification algorithms, offering insights into their suitability for early detection. In this study, we leverage Generative Adversarial Networks (GANs) for data augmentation in medical imaging, enhancing the quality and diversity of brain images associated with AD. Various classification algorithms, including AlexNet, GoogleNet, VGG 16, VGG 19, ResNet 18, ResNet 50, ResNet 101, ShuffleNet, MobileNet, and DenseNet 201, are employed for robust AD detection. Our experiments demonstrate improved classification accuracy and robustness due to GAN-based data augmentation. Among the classification algorithms, ResNet 50 and DenseNet 201 exhibit superior performance, showcasing their potential in accurate and reliable early AD diagnosis.
Keywords
GANs, Alzheimer’s Disease, Data Augmentation, Classification Algorithms, ResNet 50, DenseNet 201.
Subscription
Login to verify subscription
User
Font Size
Information
- M.S. Albert, M.B. Moss, R. Tanzi and K. Jones, “Preclinical Prediction of AD using Neuropsychological Tests”, Journal of the International Neuropsychological Society, Vol. 7, No. 5, pp. 631-639, 2001.
- B. Magnin, L. Mesrob, S. Kinkingnehun, M. P.-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehericy, and H. Benali, “Support Vector Machine-based Classification of AD from Whole-Brain Anatomical MRI”, Neuroradiology, Vol. 51, No. 2, pp. 73-83, 2008.
- Kirsi Juottonen, Mikko P. Laakso, Kaarina Partanen and Hilkka Soininen, “Comparative MR Analysis of the Entorhinal Cortex and Hippocampus in Diagnosing Alzheimer Disease”, American Journal of Neuroradiology, Vol. 20, No. 1, pp. 139-144, 1999.
- D.H. Ye, K.M. Pohl and C. Davatzikos, “Semi-Supervised Pattern Classification: Application to Structural MRI of AD”, Proceedings of IEEE International Workshop on Pattern Recognition in Neuroimaging, pp. 1- 4, 2011.
- N. Hu, J. Tai Yu, L. Tan, Y. Wang, L. Sun and L. Tan, “Nutrition and the Risk of AD”, BioMed Research International, Vol. 2013, pp. 1-12, 2013.
- J. Xie and C. Wang, “Using Support Vector Machines with a Novel Hybrid Feature Selection Method for Diagnosis of Erythemato-Squamous Diseases”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5809-5815, 2011.
- J.S. Sartakhti, M.H. Zangooei and K. Mozafari, “Hepatitis Disease Diagnosis using a Novel Hybrid Method based on Support Vector Machine and Simulated Annealing (SVMSA)”, Computer Methods and Programs in Biomedicine, Vol. 108, No. 2, pp. 570-579, 2012.
- R.O. Duda, P.E. Hart and D.G. Stock, “Pattern Classification”, 2nd Edition, Wiley-Interscience, 2001.
- H. Benjamin Fredrick David and S. Antony Belcy, “Heart Disease Prediction using Data Mining Techniques”, ICTACT Journal on Soft Computing, Vol. 9, No. 1, pp. 1817-1823, 2018.
- D.C. Yadav and S. Pal, “Prediction of Heart Disease using Feature Selection and Random Forest Ensemble Method”, International Journal of Pharmaceutical Research, Vol. 12, No. 4, pp. 56-66, 2020.
- X. Yanwei, J. Wang, Z. Zhao and Y. Gao, “Combination Data Mining Models with New Medical Data to Predict Outcome of Coronary Heart Disease”, Proceedings of International Conference on Convergence Information Technology, pp. 868-872, 2007.
- R. Andrea and R. Eduardo, “Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases”, IEEE Transactions on Medical Imaging, Vol. 33, No. 6, pp. 1262-1274, 2014.
- Manhua Liu, Daoqiang Zhang, Pew-Thian Yap and Dinggang Shen, “Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of AD”, Proceedings of International Workshop on Machine Learning in Medical Imaging, pp. 27-35, 2012.
- P. Ghosh, F.J.M. Shamrat and A.A. Khan, “Optimization of Prediction Method of Chronic Kidney Disease using Machine Learning Algorithm”, Proceedings of International Joint Symposium on Artificial Intelligence and Natural Language Processing, pp. 1-6, 2020.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 47
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0