International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning-Based Initial Screening and Prompt Detection of Alzheimer's Disease using MRI Brain Scans |
|---|---|
| ABSTRACT | Alzheimer's disease is a degenerative disease of brain and the predominant contributor of dementia worldwide. Prior and accurate diagnosis is essential to improving patient outcomes, yet conventional diagnostic approaches often fall short in detecting subtle early-stage changes. This paper portrays a CNN-based deep multilayer learning mechanism for the automated incipient identification and categorization of Alzheimer disease from MRI brain scans. The proposed approach incorporates image refinement, automatic feature acquisition, transfer learning, and Grad-CAM visualization to segregate brain MRI images into Alzheimer's-affected and healthy categories. Experimental evaluation on the Alzheimer's disease neuroimaging Initiative (ADNI) dataset demonstrates strong classification accuracy, sensitivity, specificity, and AUC-ROC performance. The observed outcomes suggest that the developed framework offers a reliable, interpretable, and clinically applicable tool for supporting early Alzheimer's disease diagnosis. The paper aims to reduce the dependency on manual diagnosis by offering a smart, sophisticated and intelligent decision-support solution for Alzheimer’s disease prediction The platform employs multilayer learning algorithms, mainly convolutional artificial neural networks, for automatic extraction of discriminative features from the dataset without relying heavily on manual analysis. The framework includes distinct phases, including data cleaning, preprocessing, feature learning, model training, and performance testing. In addition, optimization strategies are implemented to boost the steadiness of the model and reduce over fitting issues during training. |
| AUTHOR | RUBEENA KHANUM, PROF. SHANMUKASWAMY C.V Student, M. Tech, Dept. of CSE, Sridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India Associate Professor, Dept. of CSE, Sridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406015 |
| pdf/15_Deep Learning-Based Initial Screening and Prompt Detection of Alzheimer's Disease using MRI Brain Scans.pdf | |
| KEYWORDS | |
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