ArticleScientific reports2025
A hybrid filtering and deep learning approach for early Alzheimer's disease identification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Combining generative large language models and pretrained language models for predicting acute respiratory distress syndrome progression.Medical & biological engineering & computing · 2026Article
- Explainable Patient-Level Cognitive Impairment Screening via Temporal, Semantic, and Psycholinguistic Multimodal AI.Journal of Intelligence · 2026Article
- Interpretable self-supervised transformers for resting-state EEG analysis in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Developing a QSPR model for Alzheimer's drugs using topological indices and M-polynomial: A computational study.Scientific reports · 2025Article
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7 authors.
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Abstract
Alzheimer's disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introduces an innovative hybrid filtering approach with a deep transfer learning model for detecting Alzheimer's disease utilizing brain imaging data. The hybrid filtering method integrates the Adaptive Non-Local Means filter with a Sharpening filter for image preprocessing. Furthermore, the deep learning model used in this study is constructed on the EfficientNetV2B3 architecture, augmented with additional layers and fine-tuning to guarantee effective classification among four categories: Mild, moderate, very mild, and non-demented. The work employs Grad-CAM++ to enhance interpretability by localizing disease-relevant characteristics in brain images. The experimental assessment, performed on a publicly accessible dataset, illustrates the ability of the model to achieve an accuracy of 99.45%. These findings underscore the capability of sophisticated deep learning methodologies to aid clinicians in accurately identifying Alzheimer's disease.
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