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Related Experiment Video

Updated: Aug 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Khushboo Munir¹, Fabrizio Frezza¹, Antonello Rizzi¹

¹Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces advanced deep learning models for brain tumor detection using MRI scans. Novel architectures significantly improve segmentation accuracy, aiding in earlier and more consistent diagnosis.

Keywords:

artificial intelligence brain tumors clinical diagnosis convolutional neural networks deep learning

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Last Updated: Aug 22, 2025

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Published on: November 30, 2022

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Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Medical images are crucial for diagnosing and monitoring diseases like brain tumors.
Accurate segmentation of brain tumors from Magnetic Resonance (MR) images is essential for effective treatment planning.
Current deep learning methods show promise in extracting tumor features for clinical diagnosis.

Purpose of the Study:

To propose novel deep neural network architectures for automated brain tumor detection and segmentation.
To enhance the performance and consistency of brain tumor identification from MR images.
To compare the efficacy of proposed convolutional neural network and inception module-based architectures against baseline models.

Main Methods:

Development and testing of new deep neural network architectures, including MI-Unet and Hybrid Unet, incorporating inception modules.
Utilizing convolutional neural networks for feature extraction and segmentation of brain tumors in MR images.
Comparative analysis of proposed architectures against a baseline Unet model using metrics like dice score, sensitivity, and specificity.

Main Results:

MI-Unet demonstrated a 7.5% increase in dice score, 23.91% in sensitivity, and 7.09% in specificity compared to the baseline.
Depth-wise separable MI-Unet showed improvements of 10.83% in dice score, 2.97% in sensitivity, and 12.72% in specificity.
Depth-wise separable hybrid Unet achieved the highest performance gains: 15.45% in dice score, 20.56% in sensitivity, and 12.22% in specificity over the baseline.

Conclusions:

The proposed deep neural network architectures, particularly depth-wise separable hybrid Unet, significantly enhance brain tumor segmentation accuracy.
Automated screening procedures using these advanced models offer more robust and consistent identification of brain tumors.
These findings support the clinical utility of deep learning for improved brain tumor diagnosis and patient management.