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Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model

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Abstract

Alzheimer's disease (AD), which occurs as a result of the loss of cognitive functions in the brain, causes near-forgetfulness in the case and dementia in subsequent processes. Dataset consists of MR images containing four phases of AD. The dataset was re-enhanced separately with DeepDream, fuzzy color image enhancement, hypercolumn techniques. Visual Geometry Group-16 (VGG-16) deep learning model is used in the enhancing process and deep features are combined. Linear Regression is used for the selection of efficient features. The Support Vector Machine is preferred as a classifier. With the proposed approach, the classification achievement was obtained as 100% in Mild Dementia, 99.94% in Moderate Dementia, 100% in non-Dementia, 99.94% in Very Mild Dementia. The overall accuracy was 99.94%. The proposed approach increased the prediction success in detecting Alzheimer's stages by re-enhancing MR images. Thus, an efficient early diagnosis model was realized at an affordable cost for individuals likely to progress with dementia.

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Authors and Affiliations

  1. Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey

    Mesut Toğaçar

  2. Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey

    Zafer Cömert

  3. Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey

    Burhan Ergen

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  1. Mesut Toğaçar
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  2. Zafer Cömert
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Toğaçar, M., Cömert, Z. & Ergen, B. Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model. Neural Comput & Applic 33, 9877–9889 (2021). https://doi.org/10.1007/s00521-021-05758-5

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