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J. Imaging
Volume 11
Issue 10
10.3390/jimaging11100343
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Article

Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence

by
Christos Ch. Andrianos
,
Spiros A. Kostopoulos
,
Ioannis K. Kalatzis
,
Dimitris Th. Glotsos
,
Pantelis A. Asvestas
,
Dionisis A. Cavouras
and
Emmanouil I. Athanasiadis
*
Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, 12241 Athens, Greece
*
Author to whom correspondence should be addressed.
J. Imaging 2025, 11(10), 343; https://doi.org/10.3390/jimaging11100343
Submission received: 18 July 2025 / Revised: 8 September 2025 / Accepted: 30 September 2025 / Published: 2 October 2025
(This article belongs to the Section Medical Imaging)
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Abstract

Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions. The accurate determination of astrocytoma grade is critical for patient management, especially in high-malignancy-grade cases. This study proposes a fully optimized Convolutional Neural Network (CNN) for the classification of astrocytoma MRI slices across the three malignant grades (G2–4). The training dataset consisted of 1284 pre-operative axial 2D MRI slices from T1-weighted contrast-enhanced and FLAIR sequences derived from 69 patients. To provide the best possible model performance, an extensive hyperparameter tuning was carried out through the Hyperband method, a variant of Successive Halving. Training was conducted using Repeated Hold-Out Validation across four randomized data splits, achieving a mean classification accuracy of 98.05%, low loss values, and an AUC of 0.997. Comparative evaluation against state-of-the-art pre-trained models using transfer learning demonstrated superior performance. For validation purposes, the proposed CNN trained on an altered version of the training set yielded 93.34% accuracy on unmodified slices, which confirms the model’s robustness and potential use for clinical deployment. Model interpretability was ensured through the application of two Explainable AI (XAI) techniques, SHAP and LIME, which highlighted the regions of the slices contributing to the decision-making process.
Keywords: astrocytoma; convolutional neural network; hyperparameters optimization; explainable artificial intelligence astrocytoma; convolutional neural network; hyperparameters optimization; explainable artificial intelligence

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MDPI and ACS Style

Andrianos, C.C.; Kostopoulos, S.A.; Kalatzis, I.K.; Glotsos, D.T.; Asvestas, P.A.; Cavouras, D.A.; Athanasiadis, E.I. Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence. J. Imaging 2025, 11, 343. https://doi.org/10.3390/jimaging11100343

AMA Style

Andrianos CC, Kostopoulos SA, Kalatzis IK, Glotsos DT, Asvestas PA, Cavouras DA, Athanasiadis EI. Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence. Journal of Imaging. 2025; 11(10):343. https://doi.org/10.3390/jimaging11100343

Chicago/Turabian Style

Andrianos, Christos Ch., Spiros A. Kostopoulos, Ioannis K. Kalatzis, Dimitris Th. Glotsos, Pantelis A. Asvestas, Dionisis A. Cavouras, and Emmanouil I. Athanasiadis. 2025. "Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence" Journal of Imaging 11, no. 10: 343. https://doi.org/10.3390/jimaging11100343

APA Style

Andrianos, C. C., Kostopoulos, S. A., Kalatzis, I. K., Glotsos, D. T., Asvestas, P. A., Cavouras, D. A., & Athanasiadis, E. I. (2025). Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence. Journal of Imaging, 11(10), 343. https://doi.org/10.3390/jimaging11100343

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