Source of Publication
Frontiers in Public Health
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.
Frontiers Media SA
Medicine and Health Sciences
MRI, CNN, segmentation, classification, brain tumor classification, deep neural networks, pre-trained models, transfer learning
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Taher, Fatma; Shoaib, Mohamed R.; Emara, Heba M.; Abdelwahab, Khaled M.; El-Samie, Fathi E. Abd; and Haweel, Mohammad T., "Efficient framework for brain tumor detection using different deep learning techniques" (2022). All Works. 5504.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series