JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Explainable brain tumor classification using an attention-augmented CNN and Grad-CAM
Abstract
Aims: This paper explores clinical explainability of neural network architectures to classify brain neoplasma using the magnetic resonance imaging patterns.
Methods: We tested various architectures, such as a standard Convolutional Neural Network as our reference network and our attention-augmented CNN model, as well as pre-trained networks (i.e., MobileNetV2, InceptionV3, and Xception) based on a quaternary-class MRI repository of glioma, meningioma, pituitary adenoma, and healthy cases. Adam optimization algorithm was used to train the model and the performance was evaluated using Gradient-weighted Class Activation Mapping visualization and quantitative outcomes of the metrics that included F1-score, classification accuracy, precision rates, sensitivity values, and confusion matrix.
Results: The architecture of MobileNetV2 presented better results since it attained a 95.7 percent accuracy in classification, and the precision, sensitivity and F1-score metrics of the model were harmonized. The attention-enhanced CNN had the same level of efficacy, with Grad-CAM heatmaps always showing pathologically significant areas, which confirmed the diagnostic reliability of these structures.
Conclusion: MobileNetV2 (pre-trained) architecture has outstanding performance in terms of MRI based neoplasm classification. Moreover, the combination of the attention mechanisms and Grad-CAM interpretability techniques can improve the accuracy and transparency of the diagnosis, and, therefore, they can be further adopted in the clinical diagnostic processes.

Keywords : MRI, deep learning, brain tumor, transfer learning, explainable AI

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Article available in :
Volume 4, Issue 2, 2026
Page : 77-82
https://doi.org/10.51271/JCEEES-0040
Article Information
Received : 12 Oca 2026
Accepted : 21 May 2026
Published : 13 Eki 2026
Corresponding Author :

Maral Amer Mustafa: Graduate Student, Department of Computer Engineering, Faculty of Natural and Applied Sciences, Gazi University, Ankara, Turkiye

[email protected]
Citation : Mustafa, M. A., Erdem, O. A., & Söğüt, E. (2026). Explainable brain tumor classification using an attention-augmented CNN and Grad-CAM. J Comp Electr Electron Eng Sci, 4(2), 77-82. https://doi.org/10.51271/JCEEES-0040

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