AUTOMATED ALZHEIMER DISEASE PREDICTION USING MRI SCANS AND TRANSFER LEARNING TECHNIQUES
Alzheimer's Disease prediction
Disorders of the brain are one of the most difficult diseases to cure because of their fragility, the difficulty of performing procedures, and the high costs. On the other hand, the surgery itself does not have to be effective because the results are uncertain. Adults who have hypertension, one of the most common brain illnesses, may have different degrees of memory problems and forgetfulness. Depending on each patient's situation. For these reasons, it's crucial to define memory loss, determine the patient's level of decline, and determine his brain MRI scans are used to identify Alzheimer's disease. In this project, we discuss methods and approaches for diagnosing Alzheimer's disease using deep learning. The suggested approach is utilized to enhance patient care, lower expenses, and enable quick and accurate analysis in sizable investigations. Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early detection of the disease can improve patient outcomes, and brain MRI scans have shown promise as a tool for detecting Alzheimer's disease in its early stages. In recent years, deep learning algorithms, such as pre trained model named as VGG16, have been increasingly used in Alzheimer's disease analysis from brain MRI scans. This paper proposes a VGG16-based system for Alzheimer's disease analysis from brain MRI scans. The proposed system involves several steps, including data preprocessing, feature extraction, training the VGG16 model, and evaluating its performance on a test set. The results demonstrate the effectiveness of the proposed pre trained model-based system in accurately detecting Alzheimer's disease from brain MRI scans. The proposed system has the potential to improve early detection and monitoring of Alzheimer's disease, leading to improved patient outcomes.
"AUTOMATED ALZHEIMER DISEASE PREDICTION USING MRI SCANS AND TRANSFER LEARNING TECHNIQUES ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a518-a612, July-2025, Available :https://ijsdr.org/papers/IJSDRTH01008.pdf
Volume 10
Issue 7,
July-2025
Pages : a518-a612
Paper Reg. ID: IJSDR_303640
Published Paper Id: IJSDRTH01008
Downloads: 000198
Research Area: Health Science All
Country: Thiruvarur , Tamilnadu , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
