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Hybrid Deep Learning for Brain Tumor Detection: Combining DenseNet and Custom CNN for Enhanced Accuracy.

Alex David Swaminathan¹, Almas Begum², Karthikeyan Ramamoorthy³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India.

Biomedical Engineering and Computational Biology

|December 15, 2025

Summary

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This summary is machine-generated.

A novel hybrid deep learning model combining DenseNet and a custom Convolutional Neural Network (CNN) significantly improves brain tumor detection accuracy and specificity in medical imaging.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

Deep learning models are crucial for accurate brain tumor detection in medical imaging.
Conventional models like VGG, SVM, and standard CNNs lack the real-time sensitivity and specificity required for diagnosis.

Purpose of the Study:

To develop a hybrid deep learning architecture integrating DenseNet and a custom CNN.
To enhance classification accuracy, sensitivity, and specificity for brain tumor detection in medical images.

Main Methods:

A hybrid architecture combining DenseNet's feature reuse with a custom CNN was designed.
The model was trained and tested on a preprocessed and augmented brain tumor dataset.
Performance was evaluated against benchmark models including SVM, VGG, and single DenseNet/CNN models.

Keywords:

accuracy brain tumor detection custom convolutional neural network deep learning densenet hybrid model image classification medical imaging

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Main Results:

The hybrid DenseNet-Custom CNN model achieved superior accuracy (outperforming SVM at 96%, VGG at 94%, DenseNet at 92%, and Hybrid Ensembles at ~95.2%).
Demonstrated enhanced sensitivity and specificity with improved feature representation for more accurate tumor classification.
Achieved comparable frames per second (FPS) to SVM and significantly lower than VGG.

Conclusions:

Integrating DenseNet with a custom CNN enhances brain tumor detection capabilities in medical imaging.
This hybrid approach offers a practical and diagnostically satisfactory solution by combining general deep learning with domain-specific feature engineering.
Combining multiple methods effectively improves medical image classification results.