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Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet.

Burak Taşcı¹

¹Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkey.

Diagnostics (Basel, Switzerland)

|March 11, 2023

Summary

Explainable artificial intelligence (XAI) enhances brain tumor diagnosis by improving the transparency of deep learning models. This study achieved high accuracy in detecting brain tumors using XAI methods, aiding radiologists.

Keywords:

Densenet201 GradCam INCA SVM XAI brain tumor

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Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Lack of transparency in artificial intelligence (AI) models hinders medical applications.
Explainable AI (XAI) is crucial for understanding and validating AI-driven medical diagnoses.
Accurate and rapid diagnosis of brain tumors is vital for patient outcomes.

Purpose of the Study:

To develop and evaluate an XAI-based model for faster and more accurate brain tumor diagnosis.
To enhance the safety and reliability of AI in medical diagnostics.
To improve upon existing state-of-the-art methods for brain tumor detection.

Main Methods:

Utilized DenseNet201 as a pre-trained feature extractor for brain MR images.
Employed GradCAM for tumor segmentation and an exemplar method for feature extraction.
Implemented an iterative neighborhood component (INCA) feature selector and Support Vector Machine (SVM) with 10-fold cross-validation for classification.

Main Results:

Achieved high diagnostic accuracy: 98.65% on Dataset I (Kaggle) and 99.97% on Dataset II (Figshare).
The proposed XAI model demonstrated superior performance compared to current state-of-the-art methods.
Successfully segmented tumor areas and extracted relevant features for classification.

Conclusions:

The developed XAI model offers a transparent and accurate approach to brain tumor diagnosis.
This method can serve as a valuable tool to assist radiologists in clinical practice.
XAI integration significantly improves the trustworthiness and efficacy of AI in neuro-oncology.