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A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation
John Nisha Anita1, Sujatha Kumaran2
1Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
Journal of Cancer Prevention
|October 19, 2022
Summary
This study introduces an improved method for detecting and segmenting meningioma brain tumors using a convolutional neural network (CNN). The approach enhances accuracy in identifying tumor regions from MRI scans.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurosurgery
Background:
- Meningioma brain tumor detection and segmentation is challenging due to low-intensity pixels.
- Accurate segmentation is crucial for diagnosis and treatment planning.
Purpose of the Study:
- To develop and evaluate a novel method for meningioma brain tumor detection and segmentation.
- To improve the accuracy and efficiency of meningioma analysis using deep learning.
Main Methods:
- Utilized discrete wavelet transform for image decomposition and arithmetic fusion.
- Applied data augmentation to increase sample size for a convolutional neural network (CNN) classifier.
- Employed connected component analysis for tumor region segmentation and lossless compression for the segmented image.
Main Results:
- The proposed CNN-based method successfully detected and segmented meningioma tumors.
- Experimental results demonstrated competitive performance compared to existing methods in terms of sensitivity, specificity, and segmentation accuracy.
Conclusions:
- The developed method offers a robust approach for meningioma brain tumor detection and segmentation.
- This technique shows potential for clinical application in neuro-oncology.
