Volume 10 • Issue 2 • PP: 27–35 • 2025
DenseNet201-Based Deep Transfer Learning Framework for Brain Tumor Classification in MRI Scans
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The classification of brain tumors is crucial in the context of early intervention, as appropriate and timely diagnosis can significantly influence treatment plans and patient outcomes. Radiologists have long relied on their own judgment to read medical images, a process that can be subjective and time-consuming and can lead to inter-observer variability. Artificial intelligence (AI), and particularly deep learning (DL), has transformed medical imaging and could automate diagnosis by providing prompt, reliable, and unbiased assessments. Despite this progress, many AI-based systems remain constrained in their classification accuracy and robustness across datasets. This paper presents a deep-learning model designed to improve brain-tumor classification. Its preprocessing and normalization steps reduce noise and equalize image intensity to support feature extraction. The proposed method adapts DenseNet201 for four-class classification and replaces its conventional output layer with Global Average Pooling (GAP), aiming to address vanishing gradients and overfitting while improving feature aggregation and generalization. The model was evaluated on a public Figshare dataset and a local dataset comprising 3,504 T1-weighted contrast-enhanced MRI images. It achieved 100% overall accuracy, outperforming the comparison methods. These results suggest that architectural modifications combined with preprocessing can support automated brain-tumor classification under the evaluated conditions.
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References
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