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Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification.

Syed Muddusir Hussain¹, Jawwad Sami Ur Rahman¹, Faraz Akram¹

¹Biomedical Engineering Department, Riphah International University, I-14 Campus, Islamabad 45210, Pakistan.

Diagnostics (Basel, Switzerland)

|May 4, 2026

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This summary is machine-generated.

Optimized deep learning models improve brain tumor classification from MRI scans. This CNN achieved 95.35% accuracy with lower computational cost, outperforming deeper networks.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Brain tumors pose significant diagnostic challenges.
Accurate tumor identification via Magnetic Resonance Imaging (MRI) is crucial for treatment.
Developing efficient classification models is a key medical requirement.

Purpose of the Study:

To design and optimize a Convolutional Neural Network (CNN) model for brain tumor classification using MRI.
To enhance classification accuracy and reduce computational cost compared to existing architectures.

Main Methods:

An optimized CNN model with dropout layers and hyperparameter tuning was developed.
A dataset of 640 MRI scans (320 tumor, 320 non-tumor) was utilized.
The model was trained using the Adam optimizer with a learning rate of 0.001 and benchmarked against VGG-19, Inception V3, ResNet-10, and ResNet-50.

Keywords:

MRI images brain tumor convolutional neural network hyperparameter precise tumor detection tumor detection

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Main Results:

The optimized CNN achieved a peak training accuracy of 97.77% and a test accuracy of 95.35%.
The model demonstrated superior performance and significantly lower training time compared to deeper architectures.
High validation stability was observed, ranging from 92.25% to 95.35%.

Conclusions:

Hyperparameter optimization and regularization are more critical than model depth for MRI tumor classification.
The lightweight CNN offers a feasible solution for real-time applications due to its high accuracy and low computational complexity.