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 | A Survey on Alzheimer's Disease Detection Using Deep Learning |
|---|---|
| ABSTRACT | Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly affects memory, cognition, and daily functioning. Early detection of Alzheimer’s disease is essential for improving patient care and slowing disease progression. Magnetic Resonance Imaging (MRI) plays an important role in identifying structural brain abnormalities associated with Alzheimer’s disease. In recent years, deep learning techniques have shown promising results in analyzing MRI images for automated disease detection. This survey paper presents a comprehensive review of machine learning and deep learning techniques used for Alzheimer’s disease diagnosis. The study discusses traditional machine learning approaches, transfer learning models (MobileNet, InceptionV3, ResNet 101), and ensemble learning techniques applied in recent research. As the traditional machine learning techniques are lagging behind computationally and economically, researchers moved towards deep learning approaches. Furthermore, the paper reviews commonly used datasets such as ADNI, OASIS, and Kaggle Alzheimer MRI datasets. A comparative analysis of state-of-the-art methods is also presented. This survey reviews recent deep learning approaches for Alzheimer Detection published between 2017[1] and 2025[14] with 85% to 93.33% of accuracy progress. Research limitations including unimodal approaches, binary classification dominance, and lack of clinical deployment are discussed. Finally, the survey identifies research gaps and highlights potential future research directions for improving automated Alzheimer detection systems. |
| AUTHOR | TIKOLE SHANTANU SHRIKANT, PROF. DR. B. D. PHULPAGAR Student, Department of Computer Engineering, P. E. S. Modern College of Engineering, Savitribai Phule Pune University, Pune, India Assistant Professor, Department of Computer Engineering, P. E. S. Modern College of Engineering, Savitribai Phule Pune University, Pune, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404100 |
| pdf/100_A Survey on Alzheimer's Disease Detection Using Deep Learning.pdf | |
| KEYWORDS | |
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