Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

Keywords: Brian tumor, Magnetic resonance imaging, Image enhancement, Image segmentation, Convolutional neural network


The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC.


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Author Biographies

Mohammed Al-Mukhtar, Electronic Computer Center, University of Baghdad,Baghdad, Iraq

Mohammed Al-Mukhtar is a Lecturer in the Department of Survey Engineering at the University of Baghdad, Iraq, where he has worked since 2020. He got the M.Sc. degree in Information and Communication Engineering from Huazhong University of Science and Technology (HUST) in Wuhan, China. His research interests include deep learning algorithms for medical image analysis, computer vision, object recognition, face-mesh detection and object segmentation.

Ameer H. Morad, Department of Software Engineering, Faculty of Engineering, Gilgamesh Ahliya University,Baghdad, Iraq

Ameer H. Morad is a Professor at Department of Medical Instruments Engineering Technology, Faculty of Engineering Technology, and Gilgamesh University. He got the B.Sc. degree in Electrical Engineering, the M.Sc. in Computer Engineering, and the Ph.D. degree in Computer Engineering. His research interests are in Pattern Recognition, Digital Image Processing, Object Recognition, and Computer Vision.

Hussein L. Hussein, Department of Computer Science, College of Education for Pure Science-Ibn Al Haitham, University of Baghdad,Baghdad, Iraq

Hussein L. Hussein is a Assistant Prof. at the Department of computer science, College of Education for Pure Sciences (Ibn al-Haitham), University of Baghdad. He got the B.Sc. and M.Sc. degrees from University of Technology, Baghdad, Iraq, and the Ph.D. degree from University of Babylon. His research interests are in
digital image processing and Data security.

Mina H. Al-hashimi, Department of Computer Science and Information System, Al-Mansour University College,Baghdad, Iraq

Mina H. Al-hashimi is an Assistant Lecturer at the Department of Computer Science and Information System, Al-Mansour University College, Baghdad, Iraq. She got the B.Sc. degree in Computer Science from Al-Mansour University College in 2019, and the M.Sc. degree in Computer Science from the Institute of Informatics in 2022. Her research interests include deep learning models, computer vision, Classification problems and Machine Learning Algorithms.


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How to Cite
Al-Mukhtar, M., Morad, A. H., Hussein, H. L. and Al-hashimi, M. H. (2024) “Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12(1), pp. 88-99. doi: 10.14500/aro.11333.