AUTHOR=Taher Fatma , Shoaib Mohamed R. , Emara Heba M. , Abdelwahab Khaled M. , Abd El-Samie Fathi E. , Haweel Mohammad T. 

TITLE=Efficient framework for brain tumor detection using different deep learning techniques

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.959667

DOI=10.3389/fpubh.2022.959667

ISSN=2296-2565

ABSTRACT=The brain tumor is an urgent malignant condition 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 profession in medical imaging for the medical diagnosis of a lot of symptoms. In this paper, we introduce a pre-trained transfer learning-based models, in addition to, a Convolutional Neural Network (CNN) called, BRAIN-TUMOR-net trained from scratch to classify brain magnetic resonance (MR) images into tumor or normal cases. A comparison between the pre-trained Inceptionresnetv2, Inceptionv3, and Resent50 models and the proposed BRAIN-TUMOR-net has been introduced. The performance of the proposed approaches is tested on three publicly available MRI datasets. The experimental results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100\%, 97\%, and 84.78\% accuracies levels for the third, second, and first MRI datasets, respectively. Also, a K-Folds cross-validation technique has been used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.