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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
Article Published : 9239
Total Authors : 24597
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| Paper Title: | A Lightweight Advanced Hybrid Transformer Model for Early Detection of Alzheimer’s Disease Using Neurological Medical Images |
| Authors Name: | Saikat Mondal , Venkatesh G |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211688
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| Published Paper Id: | IJRTI2604272 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Alzheimer’s disease is a progressive neurodegenerative disorder that leads to cognitive decline and memory impairment. Early detection of Alzheimer’s disease is crucial for enabling timely clinical intervention and improving patient outcomes. Conventional diagnostic methods rely heavily on manual analysis of brain magnetic resonance imaging scans, which can be time-consuming and prone to subjective interpretation errors. This study presents a deep learning-based framework for automated classification of Alzheimer’s disease stages using brain magnetic resonance imaging images. The proposed approach utilizes an efficient MobileViT-based architecture that combines convolutional operations with lightweight transformer modules to capture both local spatial features and global contextual information. Convolutional layers extract fine-grained visual patterns, while transformer components model long-range dependencies across the image. Data augmentation techniques such as random rotation and horizontal flipping are applied to enhance model generalization. The model is trained using transfer learning with selective layer unfreezing and progressive fine-tuning, and optimized using the AdamW optimizer with a cosine annealing learning rate scheduler. Experimental results demonstrate that the proposed model effectively learns discriminative representations from brain magnetic resonance imaging scans and achieves strong classification performance across Alzheimer’s disease categories. The framework highlights the potential of MobileViT-based architectures for medical image analysis and supports early diagnosis of neurodegenerative diseases. |
| Keywords: | Alzheimer disease, deep learning, convolutional neural networks, transformers, medical image classification, brain MRI, MobileViT |
| Cite Article: | "A Lightweight Advanced Hybrid Transformer Model for Early Detection of Alzheimer’s Disease Using Neurological Medical Images", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b985-b989, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604272.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604272
Registration ID:211688
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b985-b989 Country: Bengaluru, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604272 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604272 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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