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Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation
Ahmed M Gab Allah1,2, Amany M Sarhan1, Nada M Elshennawy1
1Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt.
Diagnostics (Basel, Switzerland)
|December 24, 2021
Summary
This study introduces a new AI framework for classifying brain tumors from MRI scans. Using advanced augmentation techniques, the system achieved 98.54% accuracy in identifying glioma, meningioma, and pituitary tumors.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Accurate brain tumor classification is crucial for effective treatment planning.
- Convolutional Neural Networks (CNNs) aid in classifying brain tumors from MRIs but can overfit with limited data.
- Data augmentation is a key strategy to improve CNN performance in medical image analysis.
Purpose of the Study:
- To evaluate a novel framework for brain tumor MRI classification.
- To assess the efficacy of a VGG19 feature extractor combined with a progressive growing generative adversarial network (PGGAN) for data augmentation.
- To improve the accuracy of classifying common brain tumor types.
Main Methods:
- Utilized a VGG19 feature extractor and three distinct classifiers.
- Employed a progressive growing generative adversarial network (PGGAN) to generate synthetic brain tumor MRIs.
- Trained and validated the model on a dataset of brain tumor MRIs.
Main Results:
- The proposed framework achieved a high classification accuracy of 98.54% for gliomas, meningiomas, and pituitary tumors.
- The PGGAN augmentation effectively addressed the issue of limited training data, enhancing model robustness.
- The system demonstrated superior performance compared to previous brain tumor classification studies.
Conclusions:
- The developed framework offers a highly accurate and efficient method for brain tumor classification from MRIs.
- PGGAN-based data augmentation is effective in improving deep learning model performance for medical imaging tasks.
- This approach holds significant potential for improving diagnostic accuracy and patient outcomes in neuro-oncology.
