Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network
Bayoumi, E., Abd-Ellah, M., Khalaf, A., Gharieb, R. (2021). Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network. EKB Journal Management System, 41(2), 19-30. doi: 10.21608/jaet.2020.42896.1051
Esraa Bayoumi; mahmoud khaled Abd-Ellah; Ashraf A. M. Khalaf; Reda R. Gharieb. "Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network". EKB Journal Management System, 41, 2, 2021, 19-30. doi: 10.21608/jaet.2020.42896.1051
Bayoumi, E., Abd-Ellah, M., Khalaf, A., Gharieb, R. (2021). 'Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network', EKB Journal Management System, 41(2), pp. 19-30. doi: 10.21608/jaet.2020.42896.1051
Bayoumi, E., Abd-Ellah, M., Khalaf, A., Gharieb, R. Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network. EKB Journal Management System, 2021; 41(2): 19-30. doi: 10.21608/jaet.2020.42896.1051

Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network

Journal of Advanced Engineering Trends
Article 2, Volume 41, Issue 2, July 2021, Page 19-30  XML PDF (1.24 MB)
Document Type: Original Article
DOI: 10.21608/jaet.2020.42896.1051
View on SCiNiTO View on SCiNiTO
Authors
Esraa Bayoumiorcid 1; mahmoud khaled Abd-Ellah email orcid 2; Ashraf A. M. Khalaforcid 3; Reda R. Gharieb4
1Electrical Engineering Department, Egyptian Academy for Engineering and Advanced Technology,Cairo, Egypt
2Electronic and communication department, Madina Higher Institute for Engineering and Technology
3Electrical Engineering Department, Faculty of Engineering, Minia 61111, Egypt
4Faculty of Engineering, Assiut University
Abstract
Brain tumor detection successfully in early-stage plays important role in improving patient treatment and survival. Evaluating magnetic resonance imaging (MRI) images manually is a very difficult task due to the numerous numbers of images produced in the clinic routinely. So, there is a need for using a computer-aided diagnosis (CAD) system for early detection and classification of brain tumors as normal and abnormal. The paper aims to design and evaluate the convolution neural network (CNN) Transfer Learning state-of-the-art performance proposed for image classification over the recent years. Five different modifications have been applied to five different famous CNN to know the most effective modification. Five-layer modifications with parameter tuning are applied for each architecture providing a new CNN architecture for brain tumor detection. Most brain tumor datasets have a small number of images to train the deep learning structure. Therefore, two datasets are used in the evaluation to ensure the effectiveness of the proposed structures. Firstly, a standard dataset from the RIDER Neuro MRI database including 349 brain MRI images with 109 normal images and 240 abnormal images. Secondly, a collection of 120 brain MRI images including 60 abnormal images and 60 normal images. The results show that the proposed CNN Transfer Learning with MRI’s can learn significant biomarkers of brain tumor, however, the best accuracy, specificity, and sensitivity gained is 100% for all of them.
Keywords
Brain Tumor; CNN Transfer Learning; Deep Learning; CNN; Tumor Classification
Statistics
Article View: 518
PDF Download: 817