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Updated: Apr 18, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

Treeve White¹, Sareh Rowlands²

¹Computer Science Department, University of Exeter, North Park Road, Exeter, EX4 4RN, United Kingdom. treeve.white@outlook.com.

International Journal of Computer Assisted Radiology and Surgery

|April 16, 2026

Summary

CogniNet, a new deep learning model, accurately classifies Alzheimer's Disease progression using MRI scans. This explainable AI approach aids clinical diagnosis by highlighting influential brain regions.

Keywords:

Alzheimer’s disease Convolutional neural network Magnetic resonance imaging Mild cognitive impairment

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Neuroimaging
Artificial Intelligence
Medical Diagnostics

Background:

Alzheimer's Disease (AD) diagnosis faces challenges in early detection and clinical decision-making.
Current research often uses binary classification and lacks model interpretability and clinical usability.

Purpose of the Study:

To introduce CogniNet, a novel deep learning model for Alzheimer's progression classification.
To address limitations in existing AI models for AD diagnosis, focusing on interpretability and clinical utility.

Main Methods:

Developed CogniNet, a convolutional neural network (CNN) combining VGGNet19 and DenseNet201 architectures.
Utilized T1-weighted MRI scans for four-way classification of Alzheimer's progression.
Employed Grad-CAM for generating attention maps to enhance model interpretability.

Main Results:

CogniNet achieved 98% accuracy and 98% sensitivity on 3,200 unseen MRI slices.
Demonstrated superior performance compared to established CNN architectures and prior research.
Grad-CAM provided visual explanations for model predictions.

Conclusions:

CogniNet is a high-performing, explainable deep learning model for AI-assisted neuroimaging diagnostics.
Interpretable outputs from Grad-CAM foster clinical trust and adoption.
The model shows promise for improving Alzheimer's Disease diagnosis and management.