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Efficient framework for brain tumor detection using different deep learning techniques
Fatma Taher1, Mohamed R Shoaib2, Heba M Emara2
1College of Technological Innovative, Zayed University, Abu Dhabi, United Arab Emirates.
Frontiers in Public Health
|December 19, 2022
Summary
A novel deep learning model, BRAIN-TUMOR-net, accurately classifies brain tumors in MRI scans. This convolutional neural network achieved high accuracy, outperforming other models in detecting malignant growths.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Brain tumors are a critical malignancy resulting from uncontrolled cell proliferation.
- Current tumor classification relies on post-surgical biopsies, delaying diagnosis.
- Deep learning shows promise in enhancing medical image analysis for diagnosis.
Purpose of the Study:
- To introduce and evaluate deep learning models for classifying brain magnetic resonance images (MRIs) as either normal or tumorous.
- To compare a novel Convolutional Neural Network (CNN), BRAIN-TUMOR-net, against established transfer-learning models.
Main Methods:
- Developed BRAIN-TUMOR-net, a CNN trained from scratch for brain tumor classification.
- Compared BRAIN-TUMOR-net with pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models.
- Validated model performance on three public MRI datasets using k-fold cross-validation and unsupervised clustering for segmentation.
Main Results:
- BRAIN-TUMOR-net demonstrated superior performance across all three MRI datasets.
- Achieved accuracy levels of 100%, 97%, and 84.78% on the tested datasets.
- K-fold cross-validation confirmed robust classification capabilities.
Conclusions:
- BRAIN-TUMOR-net is a highly effective deep learning model for brain tumor classification from MRI data.
- The proposed model offers a promising non-invasive approach to aid in the early diagnosis of brain tumors.
- Deep learning, particularly CNNs, significantly advances medical imaging analysis in oncology.
