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Enhanced computerised diagnosis of Alzheimer’s disease from brain MRI images using a classifier merger strategy

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Abstract

This paper targets a novel classifier merging methodology for automated and precise judgement of Alzheimer's disease. The six diverse joining rules (mean, median, product, maximum, minimum, and voting) are presented with their significance in the consolidating of classifiers with that of the individual classifiers. The approval of the proposed combination procedure is performed on benchmark ADNI dataset. The underlying emphasis uncovered the four individual classifiers out of thirteen classifiers, to be specific BayesNet, linear discriminant classifier (ldc), quadratic Bayes normal classifier (udc), and Kernel Support vector machine (KSVM) from various machine learning groups, gained the best performance values of 74.77%, 71.62%, 77.76, and 76.13% separately. The classifier merging model decorated from these four best algorithms 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 examinations effectively show that a classifier merging procedure produces better outcomes and orders subjects more precisely than base-level classifiers.

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Acknowledgements

This work is partly supported by the Research Grant of the Project entitled “Intelligent tool for automated brain disorder diagnosis from neuroimaging data: Indian perspective”, approved by National Project Implementation Unit (NPIU), MHRD, under the TEQIP-III Collaborative Research Scheme (CRS) with CRS ID: 1-5748394036.

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  1. Department of Computer Science and Engineering, BGSB University, Rajouri, Jammu and Kashmir, India

    Tawseef Ayoub Shaikh

  2. Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Rashid Ali

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Shaikh, T.A., Ali, R. Enhanced computerised diagnosis of Alzheimer’s disease from brain MRI images using a classifier merger strategy. Int. j. inf. tecnol. 14, 1791–1803 (2022). https://doi.org/10.1007/s41870-020-00606-6

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