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 Early Detection and Classification of Alzheimer’s Disease Using Brain MRI Scans |
|---|---|
| ABSTRACT | Alzheimer's Disease is a serious brain condition that slowly damages brain cells, leading to memory issues and trouble with thinking. Early and correct detection of Alzheimer's is important because it allows doctors to begin treatment earlier and helps patients have a better quality of life. Right now, doctors usually look at MRI scans by hand, but this can take a lot of time and may not always be accurate. This project introduces a new method that uses deep learning to automatically detect Alzheimer's and figure out how severe it is, using brain MRI images. The method uses a 3D Convolutional Neural Network, which helps find patterns in 3D brain scans. The system is trained on a special set of MRI scans, including real scans and ones created with GAN technology, to make sure the model works well for different stages of the disease: no change, very mild, mild, and moderate. Before training, the MRI images are cleaned up, adjusted for brightness, and resized to a standard size. The model's performance is tested with several measures like accuracy, precision, recall, F1 score, and AUC-ROC, and it shows excellent results, with an AUC score higher than 0.93. This study shows that using deep learning can improve the accuracy and reliability of Alzheimer's diagnosis. The model is strong, easy to understand, and ready to be used in real-world settings, helping doctors find the disease early and make better treatment choices. |
| AUTHOR | N.LALITHA, M. MOHAMED FAISAL P.G. Student, Department of Computer Engineering, Sir Issac Newton College of Engineering and Technology, Nagapattinam, Tamil Nadu, India Assistant Professor, Department of Computer Engineering, Sir Issac Newton College of Engineering and Technology, Nagapattinam, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403123 |
| pdf/123_Deep Learning-Based Early Detection and Classification of Alzheimer’s Disease Using Brain MRI Scans.pdf | |
| KEYWORDS | |
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