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Updated: Nov 8, 2025

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

Three-class brain tumor classification using deep dense inception residual network.

Srinath Kokkalla¹, Jagadeesh Kakarla¹, Isunuri B Venkateswarlu¹

¹IIITDM Kancheepuram, Chennai, India.

|April 26, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep dense inception residual network for accurate three-class brain tumor classification. The proposed model achieves 99.69% accuracy on brain tumor images, outperforming existing methods.

Keywords:

Brain tumor classification Deep dense network Inception residual network Three-class tumor classification

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Oncology

Background:

Accurate brain tumor classification is crucial for effective treatment.
Existing deep learning models face challenges in achieving high accuracy for three-class brain tumor classification.
Distinct tumor characteristics necessitate advanced classification methods.

Purpose of the Study:

To propose a novel deep dense inception residual network for improved three-class brain tumor classification.
To enhance the classification accuracy of brain tumor images using a customized Inception ResNet v2 architecture.
To evaluate the proposed model's performance on a large, publicly available dataset.

Main Methods:

Development of a deep dense inception residual network by customizing Inception ResNet v2.
Integration of a deep dense network and a softmax layer into the output layer.
Evaluation using key performance metrics on a dataset of 3064 brain tumor images.

Main Results:

The proposed model achieved a mean accuracy of 99.69% for three-class brain tumor classification.
The deep dense network component significantly improved classification accuracy.
The model demonstrated robust performance, even on noisy brain tumor image data.

Conclusions:

The proposed deep dense inception residual network offers superior performance for three-class brain tumor classification.
The customized architecture effectively addresses the challenges in brain image classification accuracy.
The model's high accuracy and robustness make it a promising tool for clinical applications.