Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model


  • S. Naganandhini Arulmigu Palaniandavar Arts and Science College For Women
  • P. Shanmugavadivu Gandhigram Rural Institute image/svg+xml



Alzheimer’s disease detection, MIRIAD datasets, Confusion Matrix, CNN architecture, ReLu, Dropout, Normal and Abnormal MRI images


Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.


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Alzheimer’s Australia.

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How to Cite

Naganandhini S, Shanmugavadivu P. Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Jun. 26 [cited 2024 Jul. 13];10. Available from: