Volume V | Issue 3 | 2026 | Paper ID: IJATEM-V05I03P5

An Intelligent Deep Learning Framework for Early Detection of Alzheimer Disease Using Temporal Fusion CNN

Author(s)

, D. Lakshmi

Cite This

"An Intelligent Deep Learning Framework for Early Detection of Alzheimer Disease Using Temporal Fusion CNN", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 03, pp. 34-46, 2026.

Abstract

Brain disease is a wide range of disorder that harm brain structure which leads to the disorders such as memory loss, problems that deals with thinking, emotional stress, misbehavior, changes in daily activities of a human being. This paper proposes K-means clustering based Grey Level Co-occurrence Matrix (GLCM) feature extraction with temporal fusion Convolution Neural Networks (CNN) to detect alzheimer’s disease and different disease trajectories. The term Alzheimer disease refers to brain disorder that destroys memory and rational expertise. The K- means clustering process is used to identify groups of similar data points and helps in anomaly detection. The feature extraction of GLCM analyses the relationship between pixels for capturing images which is applicable in medical diagnosis. A deep learning technique that integrates CNN to process input data, such as images or video frames, to extract important spatial features. This research significantly improves the health care system and assist as a valuable diagnostics tool in medical field and helps healthcare professionals in diagnosing alzheimer’s disease. According to the experimental outcomes attained from relevant software tool the overall accuracy is 95% and precision, recall scores as 96% respectively.

Keywords

Alzheimer’s disease detection, Data preprocessing, K-means clustering, GLCM feature extraction, Classification-Temporal fusion CNN.

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