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DiSCNet: Directional Split Convolution for compute-efficient brain tumor diagnosis.

Shahid Mohammad Ganie¹, Ishak Pacal²

¹Department of Health Information Management and Technology, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Computational Biology and Chemistry

|April 18, 2026

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A new deep learning model, DiSCNet, efficiently classifies brain tumors from MRI scans. This lightweight framework achieves high accuracy, offering a robust solution for heterogeneous brain MRI data.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain tumor classification from MRI is challenging due to patient variability and acquisition differences.
Existing deep learning models often require large capacities, limiting their generalizability.
Developing reliable and efficient models for heterogeneous brain MRI data is crucial for clinical applications.

Purpose of the Study:

To develop a compact, high-performing deep learning framework for reliable brain MRI tumor classification.
To address the limitations of large-capacity models and improve generalization across diverse MRI data.
To introduce a novel lightweight architecture, DiSCNet, for enhanced brain tumor detection.

Main Methods:

Proposed DiSCNet, a lightweight architecture featuring a novel Directional Split Convolution (DiSC) block.

Keywords:

Brain tumor classification Deep learning DiSCNet Efficient channel attention Global response normalization

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Incorporated Global Response Normalization and Efficient Channel Attention for feature stability and selectivity.

Evaluated on a unified benchmark of 17,888 MRI images across four classes (glioma, meningioma, pituitary, non-tumor) from five repositories.

Main Results:

DiSCNet achieved superior performance compared to 71 contemporary architectures, with 0.9922 accuracy, 0.9916 precision, 0.9930 recall, and 0.9923 F1-score.
The model demonstrated strong and balanced classification across all four diagnostic categories.
DiSCNet utilized only 2.78 million parameters, highlighting its efficiency.

Conclusions:

DiSCNet offers an efficient, robust, and clinically relevant solution for four-class brain tumor MRI classification.
Lightweight architectures, when carefully designed, can outperform larger models in complex medical imaging tasks.
The findings support the potential of DiSCNet for practical application in neuro-oncology diagnostics.