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Multi-level classification of Alzheimer disease using DCNN and ensemble deep learning techniques

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Abstract

The utmost popular dementia cause is because the (AD) Alzheimer’s disease. A continuous drop in mental ability is referred to as Dementia. Using the medical images of the brain, the developmental stages of AD symptom of neuropsychiatric functionality are analyzed often. Particularly in the area of classification and detection, cutting edge technologies which comprise computer algorithms, have been used for Alzheimer’s disease diagnosis treatments. To improve prediction on new data, ensemble learning applies a group of decision-making systems that uses different strategies to unite classifiers. This paper uses the combination of (DCNN) and deep ensemble learning, i.e., MobileNetV2 and LSTM using magnetic resonance images (MRI). The ADNI dataset is used for the dementia stages classification. Compared to CNN, Deep Ensemble Learning (DEL) performs better. For the evaluation, six metrics were used; accuracy, the area under the curve (AUC), F1- score, precision, recall and computational time. In addition, the calculation for specificity and sensitivity is evaluated for the performance enhancement, which shows the exact affected area. A sensitivity of 94% and a specificity of 95% are obtained in the classification, respectively.

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  1. Department of Information Technology, Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Avadi, Chennai, 60062, India

    M. Rajesh Khanna

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Rajesh Khanna, M. Multi-level classification of Alzheimer disease using DCNN and ensemble deep learning techniques. SIViP 17, 3603–3611 (2023). https://doi.org/10.1007/s11760-023-02586-z

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  • DOI: https://doi.org/10.1007/s11760-023-02586-z

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