Automated Alzheimer's disease detection from brain magnetic resonance imaging using a smart classifier fusion approach

Main Article Content

Tawseef Ayoub Shaikh
Rashid Ali


This aim of this research was to use a novel method for discriminating between Alzheimer's disease from normal controls from brain magnetic resonance imaging. Here, the six diverse connecting rules (mean, product, maximum, minimum, and voting) related to the consolidation of classifiers. The collection of the relevant benchmark data was taken from the Alzheimer's disease neuroimaging initiative (ADNI) data set for the proposal evaluation. The empirical investigations uncovered the four individual classifiers out of thirteen classifiers, viz BayesNet, linear discriminant classifier, quadratic Bayes normal classifier, and kernel support vector machine from numerous machine learning groups, gained the highest recognition percentages of 74.77%, 71.62%, 77.76, and 76.13%, respectively. These four-best performing classifiers were employed for prototyping the classifier fusion model, which displayed a much healthier performance with a shared mean error rate of 0.2123, in contrast to the mean error rate of 0.2493 before ensemble. Our analyses have shown that a smart classifier-based fusion method outperforms the base-classifier method.


Download data is not yet available.

Article Details



Chien, Y. W., Hong, S. Y., Cheah, W., Yao, L. H., Chang, Y. L., and Fu, L. (2019). An automatic assessment system for Alzheimer's disease based on speech using feature sequence generator and recurrent neural network. Scientific Reports, 9, 19597.

Dai, P., Sridhar, F. G., Bauer, M., and Borrie, M. (2015). Bagging ensembles for the diagnosis and prognostication of Alzheimer's disease. In Proceedings of Thirtieth AAAI Conference on Artificial Intelligence, pp. 3944-3951. Arizona, USA.

Dietterich, T. G. (2000). Ensemble methods in machine learning. In Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 1-15. Berlin, Germany.

Eom, J., Jang, H., Kim, S., Jang, J., and Hwang, D. (2019). Study on discrimination of Alzheimer's disease states using an ensemble neural network's model. In Proceedings of SPIE Medical Imaging 2019: Computer-Aided Diagnosis, pp. 16-21. California, USA.

He, X., Chen, L., Li, X., and Fu, H. (2019). Brain image feature recognition method for Alzheimer's disease. Cluster Computing, 22(4), 8109-8117.

Kumar, P. R., Arunprasath, T., Rajasekaran, M. P., and Vishnuvarthanan, G. (2018). Computer-aided automated discrimination of Alzheimer's disease and its clinical progression in magnetic resonance images using hybrid clustering and game theory-based classification strategies. Computers and Electrical Engineering, 72, 283-295.

Lahmiri, S., and Shmuel, A. (2018). Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer's disease. Biomedical Signal Processing and Control, 52, 414-419.

Lebedev, A. V., Westman, E., Van Westen, G. J. P., Kramberger, M. G., Lundervold, A., Aarsland, D., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Lovestone, S., and Simmons, A. (2014). Random forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness. NeuroImage: Clinical, 6, 115-125.

Li, K., Liu, Z., and Han, Y. (2012). Study of selective ensemble learning methods based on support vector machine. In Proceedings of the International Conference on Medical Physics and Biomedical Engineering, pp. 1518-1525. Paris, France.

Nisbet, R., Elder, J., and Miner, G. (2009). Model evaluation and enhancement. In Handbook of Statistical Analysis and Data Mining Applications, 2nd ed., pp. 285-312. Cambridge, Massachusetts: Academic Press.

Orimaye, S. O., Wong, J. S. M., Golden, K. J., Wong, C. P., and Soyiri, I. N. (2017). Predicting probable Alzheimer's disease using linguistic deficits and biomarkers. BMC Bioinformatics, 18, 34.

Papakostas, G. A., Savio, A., Graña, M., and Kaburlasos, V. G. (2015). A lattice computing approach to Alzheimer's disease computer-assisted diagnosis based on MRI data. Neurocomputing, 150(A), 37-42.

Pozueta, A., Rodríguez, E. R., Higuera, J. L. V., Mateo, I., Juan, P. S., Perez, S. G., Berciano, J., and Combarros, O. (2011). Detection of early Alzheimer’s disease in MCI patients by the combination of MMSE and an episodic memory test. BMC Neurology, 11, 78.

Sadek, R. A. (2013). Regional atrophy analysis of MRI for early detection of Alzheimer's disease. International Journal of Signal Processing, Image Processing and Pattern Recognition, 6(1), 49-58.

Sankari, Z., and Adeli, H. (2011). Probabilistic neural networks for EEG-based diagnosis of Alzheimer's disease using conventional and wavelet coherence. Journal of Neuroscience Methods, 197(1), 165-170.

Shaikh, T. A., Ali, R., and Beg, M. M. S. (2020). Transfer learning privileged information fuels CAD diagnosis of breast cancer. Machine Vision and Applications, 31, 9.

Shaikh, T. A., and Ali, R. (2019). Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images. Magnetic Resonance Imaging, 62(2), 167-173.

Talia, D., Trunfio, P., and Marozzo, F. (2016). Introduction to data mining. In Data Analysis in the Cloud: Computer Science Reviews and Trends, 3rd ed., pp. 1-25. Amsterdam: Elsevier.

Tessitore, A., Santangelo, G., Micco, R. D., Vitale, C., Giordano, A., Raimo, S., Corbo, D., Amboni, M., Barone, P., and Tedeschi, G. (2016). Cortical thickness changes in patients with Parkinson's disease and impulse control disorders. Parkinsonism Related Disorders, 24, 119-125.

Thal, D. R., Beach, T. G., Zanette, M., Heurling, K., Chakrabarty, A., Ismail, A., Smith, A. P. L., and Buckley, C. (2015). [18F]flutemetamol amyloid positron emission tomography in preclinical and symptomatic Alzheimer's disease: specific detection of advanced phases of amyloid-β pathology. Alzheimer's & Dementia, 11(8), 975-985.

Tierney, M. C., Yao, C., Kiss, A., and McDowell, I. (2005). Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years. Neurology, 64(11), 1853-1859.

Varatharajan, R., Manogaran, G., Priyan, M. K., and Sundarasekar, R. (2018). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing, 21(6), 681-690.

Zheng, X., Shi, J., Zhang, Q., Ying, S., and Li, Y. (2017). Improving MRI-based diagnosis of Alzheimer's disease via an ensemble privileged information learning algorithm. In Proceedings 14th IEEE International Symposium on Biomedical Imaging, pp. 456-459. Melbourne, Australia.