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Updated: Jul 3, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification.

Mithila Arman¹, Ahnaf Samin², A K M Muzahidul Islam³

¹Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.

Frontiers in Medicine

|June 5, 2026

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This summary is machine-generated.

PruDensNet, a novel neural network, efficiently classifies brain tumors using MRI scans. This parameter-efficient model achieves high accuracy, making it suitable for resource-limited clinical settings.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

Brain tumor classification from MRI is crucial for diagnosis and treatment planning.
Existing deep learning models often require significant computational resources, limiting their deployment in clinical settings.

Purpose of the Study:

To introduce PruDensNet, a parameter-efficient convolutional neural network for MRI-based brain tumor classification.
To evaluate PruDensNet's performance against established models under matched parameter budgets.

Main Methods:

Developed PruDensNet, a depthwise-separable convolutional network with attention mechanisms and GELU activations.
Implemented a reproducible data curation pipeline and a curriculum-regularized training strategy.
Utilized a no-operation padding mechanism to equalize parameter budgets across models.

Keywords:

MRI SDGs attention augmentation brain tumor classification depthwise-separable CNN parameter efficiency reproducibility

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Main Results:

PruDensNet achieved 96.05% test accuracy and 97.27% validation accuracy on a four-class Brain Tumor MRI dataset.
The model demonstrated superior performance compared to matched-capacity CNN and Transformer baselines.
PruDensNet offers a favorable accuracy-footprint trade-off with approximately 1.46 M parameters.

Conclusions:

PruDensNet presents an efficient and accurate solution for brain tumor classification from MRI.
The model's efficiency supports deployment in cost- and latency-sensitive clinical workflows.
Further validation and hardware-specific benchmarking are recommended for clinical integration.