Comparative Analysis of Deep Learning in Detecting Cognitive Impairment Associated with Alzheimer's Disease


  • Ayesha Riyajuddin Mujawar Bharati Vidyapeeth Institute of Management and Rural Development Administration, Sangli (MS), Maharashtra, India
  • C. Karthikeyini Department of Electronics and Communications Engineering, Excel Engineering College, Komarapalayam,Tamil Nadu, India
  • Durairaj Thenmozhi Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India
  • Sathyakala S. Department of Management Studies, Sona College of Technology, Salem, Tamil Nadu, India
  • Vijayalakshmi Pasupathy Department of Computer Science and Engineering, Panimalar Engineering College,Chennai, Tamil Nadu, India
  • K. Dhayalini Department of Electrical and Electronics Engineering, K.Ramakrishnan College of Engineering (Autonomous), Tiruchirappalli,Tamilnadu, India


Alzheimer's Disease, MRI scans, Machine Learning, Cognitive Impairment, Ensemble Approach


This study studies the usefulness of machine learning patterns within the early identification of cognitive impairment connected to Alzheimer's Disease the use of MRI images. A diverse dataset containing 2330 images from hospital and on-line resources provides the foundation of our observation. Four fantastic fashions—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), K-Nearest Neighbors (KNN), and the VGG16 structure—are trained and investigated. Image preparation procedures, inclusive of normalization, cropping and resizing, image augmentation, feature extraction, and statistics augmentation, are routinely performed to enhance the dataset. After undergoing extensive training, the CNN becomes the best acting model, with a 95.78% accuracy rate. The accuracy of KNN, RNN, and VGG16 is 93.5 percent, 91.9 percent, and 876 percent, respectively. The confusion matrices illuminate the subtle performance of every edition, offering insights into their skills to effectively distinguish amazing and bad occasions. The ensemble technique, utilising the complementing qualities of several fashions, gives a complete understanding of cognitive impairment. Our results contribute contributions to the growing field of machine mastering packages in scientific imaging, highlighting the significance of a holistic study for improved diagnostic accuracy. Our research represents an important step towards more potent diagnostic tools as the field develops, providing insights that go beyond the specific models used and have implications for advanced affected person outcomes in the field of Alzheimer's Disease and related neurodegenerative issues.


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How to Cite

Mujawar, A. R. ., Karthikeyini, C. ., Thenmozhi, D. ., S., S. ., Pasupathy, V. ., & Dhayalini, K. . (2024). Comparative Analysis of Deep Learning in Detecting Cognitive Impairment Associated with Alzheimer’s Disease. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 549–556. Retrieved from



Research Article