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Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning
Saransh Rohilla1, Shruti Jain1
1Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India.
Background:
Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for brain tumors are proposed in this paper.
Methods:
A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the cancer imaging archive. Due to the use of residual networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
Results:
The accuracy of tumor detection and dice similarity coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.
Conclusion:
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
Insights
This study introduces advanced deep learning models for precise brain tumor detection and segmentation using MRI scans. The novel approach significantly enhances accuracy and segmentation performance, improving oncological patient care.
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Oncology
- Neurosurgery Support
Background:
- Accurate brain tumor diagnosis and treatment planning are crucial for patient survival.
- Tumor variability poses challenges for automated segmentation methods.
- Developing precise automated detection and segmentation techniques for brain tumors is essential.
Purpose of the Study:
- To propose and evaluate novel deep learning models for automatic brain tumor detection and segmentation.
- To improve the accuracy and efficiency of brain tumor analysis in oncological patients.
- To leverage residual networks for enhanced performance in medical image analysis.
Main Methods:
- Utilized a modified ResNet50 model for tumor detection.
- Employed a ResUNet-based convolutional neural network for tumor segmentation.
- Trained and validated models on multi-contrast MRI datasets from 110 patients (TCIA, BraTS2015, BraTS2017).
Main Results:
- Achieved 96.77% accuracy for tumor detection and a 0.893 Dice Similarity Coefficient (DSC) for segmentation on the TCIA dataset.
- Demonstrated improved evaluation parameters, including accuracy and DSC, due to the use of residual networks.
- Validated model performance on BraTS2015 and BraTS2017 datasets, showing consistent and reliable results.
Conclusions:
- Residual networks significantly enhance both detection accuracy and segmentation performance (DSC).
- The proposed models show a 5.9% increase in DSC compared to the standard UNet model.
- Model accuracy improved from 92% to 96.77% on the test set, indicating superior performance.
