# A Tutorial on Explainable Image Classification for Dementia Stages Using Convolutional Neural Network and Gradient-weighted Class Activation Mapping

Source: [A Tutorial on Explainable Image Classification for Dementia Stages Using Convolutional Neural Network and Gradient-weighted Class Activation Mapping](<https://www.sota2.com/research/paper/68fa74831e209131cea621a7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-10T05:00:06.185Z

Authors: Kevin Kam Fung Yuen

Published: 2024-08-20T06:23:20.000Z

## Abstract

This paper presents a tutorial of an explainable approach using Convolutional Neural Network (CNN) and Gradient-weighted Class Activation Mapping (Grad-CAM) to classify four progressive dementia stages based on open MRI brain images. The detailed implementation steps are demonstrated with an explanation. Whilst the proposed CNN architecture is demonstrated to achieve more than 99% accuracy for the test dataset, the computational procedure of CNN remains a black box. The visualisation based on Grad-CAM is attempted to explain such very high accuracy and may provide useful information for physicians. Future motivation based on this work is discussed.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.10572>)

[PDF](<https://arxiv.org/pdf/2408.10572.pdf>)