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Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN

Nagwan Abdel Samee¹, Tahir Ahmad², Noha F Mahmoud³

¹Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Healthcare (Basel, Switzerland)

|December 23, 2022

Summary

This study introduces a simplified deep convolutional neural network (DCNN) for brain tumor (BT) classification and U-Net for real-time segmentation. The developed computer-aided diagnosis (CAD) system achieves high accuracy in classifying brain tumors from MRI scans.

Keywords:

CAD system CNN U-Net brain tumor classification clinical decision segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors (BTs) are a rare but deadly cancer.
Computer-aided diagnosis (CAD) systems for BT classification in MRI are crucial but still developing.
Accurate and efficient segmentation and classification are key challenges.

Purpose of the Study:

To develop a lightweight U-Net for real-time brain tumor segmentation.
To present a simplified deep convolutional neural network (DCNN) for automatic feature extraction and classification of segmented brain tumors.
To improve the accuracy of brain tumor classification in medical imaging.

Main Methods:

Implemented a lightweight U-Net deep learning network for precise, real-time segmentation.
Designed a simplified DCNN architecture with five convolutional layers, ReLU activation, normalization, and max-pooling for classification.
Validated the framework on the multimodal brain tumor segmentation (BRATS 2015) dataset.

Main Results:

Achieved a Dice similarity coefficient (DSC) of 88.8%, sensitivity of 89.4%, and classification accuracy of 88.6% for high-grade gliomas on BRATS 2015.
The segmentation performance is comparable to state-of-the-art methods.
Improved brain tumor image classification accuracy from 88% to 88.6% compared to previous studies.

Conclusions:

The proposed CAD framework demonstrates competitive performance in brain tumor segmentation.
The simplified DCNN architecture offers enhanced accuracy for brain tumor classification.
This research contributes to advancing automated diagnosis systems for brain tumors using MRI.