ArticleComputational intelligence and neuroscience2022
Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Fusion of transfer learning models for detection of alzheimer's disease using bidirectional long short-term memory with equilibrium optimization algorithm.Scientific reports · 2025Article
- An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.Medical & biological engineering & computing · 2025Article
- X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease.Frontiers in neurology · 2025Article
- Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.Journal of Cancer · 2024Article
- Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron.Frontiers in artificial intelligence · 2024Article
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Authors and funding
6 authors.
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Abstract
Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer's disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer's disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the
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