Improving Alzheimer's Disease Diagnosis on Brain MRI Scans with an Ensemble of Deep Learning Models
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 2024, 1, pp. 1-6
- Issue Date:
- 2024-04-15
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Alzheimer s disease AD is a widespread neurolog ical condition affecting millions globally It gradually advances leading to memory loss cognitive deterioration and a substantial decline in overall quality of life for those affected AD patients experience memory decline eroding cherished memories and straining relationships while daily tasks become challenging Numerous investigations have been conducted in this field as the timely identification of Alzheimer s disease at its initial stage is of the utmost importance A major limitation in this field is the predominant emphasis on using single fine tuned CNN architecture or comparing pre trained and custom CNN models for Alzheimer s detection often on small datasets which neglects a more comprehensive approach Using smaller datasets can negatively impact deep learning modeling accuracy due to overfitting limited representation and poor generalization This study addresses the current research problems and proposes an ensemble approach that combines predictions from various pre trained models including DenseNet 121 EfficientNet B7 ResNet 50 VGG 19 and also from a Custom CNN The model averaging ensemble method was applied a subset of the Stacking Ensemble to two ADNI datasets with Dataset I being the larger The goal was to assess the efficacy of this ensemble approach for accurate multiclass classification on ADNI datasets where it successfully identified all classes despite differing sample volumes A vast experiment was conducted on two distinct and widely recognized real world datasets resulting in accuracies of 99 96 and 98 90 respectively Finally the outcome of the research compared with recent research findings demonstrates the potential of our approach in advancing Alzheimer s disease detection by outperforming other benchmark approaches by a significant margin
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