Alzheimer’s disease prediction via an explainable CNN using genetic algorithm and SHAP values
Mohammad Zahedipour, Mohammad Saniee Abadeh, Shakila Shojaei
TL;DR
This paper introduces GASHAP, a new explainable AI method that combines genetic algorithms and SHAP values to improve the transparency of a 3D-CNN model for predicting Alzheimer’s disease from MRI scans.
Contribution
The novel GASHAP technique enhances model interpretability by identifying critical brain regions for Alzheimer’s diagnosis using SHAP values and genetic algorithms.
Findings
The 3D-CNN model effectively classifies MRI scans of Alzheimer’s patients and controls.
GASHAP highlights anatomically relevant brain regions crucial for diagnosis.
The generated brain mask outperforms previous methods in interpretability.
Abstract
Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in these black-box models raises concerns in sensitive domains such as healthcare, where understanding the knowledge acquired to derive outcomes can be challenging. To address this concern, several strategies within the field of explainable AI (XAI) have been developed to enhance model interpretability. This study introduces a novel XAI technique, GASHAP, which integrates a genetic algorithm (GA) with SHapley Additive exPlanations (SHAP) to improve the explainability of our 3D convolutional neural network (3D-CNN) model. The model is designed to classify magnetic resonance imaging (MRI) brain scans of individuals with Alzheimer’s disease and cognitively normal controls. Deep SHAP, a widely used XAI technique, facilitates the understanding of…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Machine Learning in Healthcare · Artificial Intelligence in Healthcare and Education