Efficient framework for brain tumor detection using different deep learning techniques - Taher, Fatma; Shoaib, Mohamed R.; Emara, Heba M.; Abdelwahab, Khaled M.; Abd El-Samie, Fathi E.; Haweel, Mohammad T. (Frontiers in Public Health, 2022)
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.