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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Dewan Mamun Raza
Daffodil International University
Computer Science & IT Engineering

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Comparative evaluation of transfer learning models and Grad-CAM interpretability for brain tumor detection from MRI Md. Firoz Hasan; Md. Awal Hadi; Sumaiya Nasrin; Md. Raisul Islam; Md. Atik Shahriar; Md. Hasan Moon; Tanvir Ahmed Momin; Dewan Mamun Raza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3646-3659

Abstract

Brain tumor classification plays an important role in early diagnosis and treatment planning. The current study aims to evaluate and compare the performance of five pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, MobileNet, Xception, and InceptionV3 using magnetic resonance imaging (MRI) images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. To enhance model performance and address class imbalance, transfer learning and data augmentation techniques were employed. To boost model interpretability, heatmaps of important areas in tumor classification were produced through gradient-weighted class activation mapping (Grad-CAM). MobileNet was the most accurate with 97% and was more precise and more sensitive. The Grad-CAM visualizations showed the models were attending to clinically relevant features, which increased the interpretability. This comparative study demonstrates the effectiveness of the integration of explainable artificial intelligence (XAI) in deep learning pipelines for reliable brain tumor diagnosis.
Co-Authors Md. Atik Shahriar Md. Awal Hadi Md. Firoz Hasan Md. Hasan Moon Md. Raisul Islam Sumaiya Nasrin Tanvir Ahmed Momin