September 5, 2025Discover OncologyOpen Access

Improved brain tumor classification through DenseNet121 based transfer learning

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The proposed approach achieved an average accuracy improvement of 96.90%, showing its effectiveness in brain tumor classification.
Using the DenseNet121 architecture, the system classifies brain tumors into four types: benign tumors, gliomas, meningiomas, and pituitary gland malignancies.
Model training utilized the Kaggle dataset, applying transfer learning and preprocessing techniques to enhance performance.
This automated method reduces analysis time and improves diagnostic precision over traditional approaches, emphasizing the role of technology in healthcare.

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The study demonstrates improved accuracy in brain tumor diagnosis using MRI images, indicating the potential of deep learning methods.

Rasheed et al. (2025) studied this question.

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