ArticleFrontiers in computational neuroscience2022
Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease.
Article in Frontiers in computational neuroscience, 2022. 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.
- Topology-aware hybrid graph-transformer network for Alzheimer's disease diagnosis from structural magnetic resonance imaging.Frontiers in computational neuroscience · 2026Article
- Histogram-based features track Alzheimer's progression in brain MRI.Scientific reports · 2024Article
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images.Diagnostics (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.
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