Brain Tumor Identification using Dilated U-Net based CNN
Keywords:Brain Tumor, Deep Learning Classifiers, Dilated U-Net CNN Model, Magnetic Resonance Image
The identification of brain tumor consumes time and therefore it is important to develop an automated system using an imaging technique. The classification of brain tumor into benign or malignant is performed by using Magnetic Resonance Image (MRI). From the MRI based brain tumor images, the extraction of features is essential for pattern recognition that determines the object based on the color, names, shapes, or more. Therefore, the classifiers are dependent on the strength of features such as shape, color, etc., Yet, the classifiers are dependent on the features that are extracted using deep learning classifiers which are dependent on the features that were extracted. The deep learning algorithm in the medical domain showed interest in the computer vision researchers which consumed time during the process of execution. The proposed Dilated UNet model expands the receptive field for the extraction of multi scale context information. Based on the high resolution conditions, the large scale feature maps and high-resolution conditions are generated using large scale feature maps. It provides rich spatial information that was applied for performing semantic segmentation. Semantic image segmentation is achieved using a U-Net as it adds an expansive path to generate classifications of the pixels belonging to features found in the source image. The existing Kernel based SVM model obtained accuracy of 99.15%, Non-Dominated Sorted Genetic Algorithm-Convolutional Neural Network (NSGA -CNN) obtained accuracy of 99%, Deep Elman Neural network with adaptive fuzzy clustering obtained accuracy of 98%, 3D Context Deep Supervised U-Net obtained accuracy of 92%. Whereas, the proposed Dilated U-Net-based CNN model obtained accuracy of 99.5% better when compared with the existing models.
Amin, J.; Sharif, M.; Gul, N.; Yasmin, M.; Shad, S.A. (2020). Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network, Pattern Recognition Letters, 129, 115-122.
Khan, M.A.; Ashraf, I.; Alhaisoni, M.; Damaševicius, R.; Scherer, R.; Rehman, A.; Bukhari, S.A.C. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists, Diagnostics, 10(8), 565.
Narmatha, C.; Eljack, S.M.; Tuka, A.A.R.M.; Manimurugan, S.; Mustafa, M. (2020). A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images, Journal of ambient intelligence and humanized computing, 1-9.
Sharif, M.; Amin, J.; Raza, M.; Yasmin, M.; Satapathy, S.C. (2020). An inteuated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor, Pattern Recognition Letters, 129, 150-157.
Aboelenein, N.M.; Songhao, P.; Koubaa, A.; Noor, A.; Afifi, A. (2020). HTTU-Net: Hybrid Two Track U-Net for automatic brain tumor segmentation, IEEE Access, 8, 10ey1406-101415.
Amin, J.; Sharif, M.; Gul, N.; Raza, M.; Anjum, M.A.; Nisar, M.W.; Bukhari, S.A.C. (2020). Brain tumor detection by using stacked autoencoders in deep learning, Journal of medical systems, 44(2), 1-12.
Rehman, Z.U.; Zia, M.S.; Bojja, G.R.; Yaqub, M.; Jinchao, F.; Arshid, K. (2020). Texture based localization of a brain tumor from MR-images by using a machine learning approach, Medical Hypotheses, 141, 109705.
Tandel, G.S.; Balestrieri, A.; Jujaray, T.; Khanna, N.N.; Saba, L.; Suri, J.S. (2020). Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm, Computers in Biology and Medicine, 122, 103804.
Shen, Y.; Gou, F.; Dai, Z. (2022). Osteosarcoma MRI image-assisted segmentation system base on guided aggregated bilateral network, Mathematics, 10(7), 1090.
Khan, A.R.; Khan, S.; Harouni, M.; Abbasi, R.; Iqbal, S.; Mehmood, Z. (2021). Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification. Microscopy Research and Technique, 84(7), 1389-1399.
Kang, J.; Ullah, Z.; Gwak, J. (2021). MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers, Sensors, 21(6), 2222.
Díaz-Pernas, F.J.; Martínez-Zarzuela, M.; Antón-Rodríguez, M.; González-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network, In Healthcare, 9(2), 153.
Tandel, G.S.; Tiwari, A.; Kakde, O.G. (2021). Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification, Computers in Biology and Medicine, 135, 104564.
Saeed, M.U.; Ali, G.; Bin, W.; Almotiri, S.H.; AlGhamdi, M.A.; Nagra, A.A.; Masood, K.; Amin, R.U. (2021). RMU-net: a novel residual mobile U-net model for brain tumor segmentation from MR images, Electronics, 10(16), 1962.
Zhang, W.; Yang, G.; Huang, H.; Yang, W.; Xu, X.; Liu, Y.; Lai, X. (2021). ME-Net: multiencoder net framework for brain tumor segmentation, International Journal of Imaging Systems and Technology, 31(4), 1834-1848.
Rao, C.S.; Karunakara, K. (2022). Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI, Multimedia Tools and Applications, 1-25.
Sharif, M.I.; Li, J.P.; Amin, J.; Sharif, A. (2021). An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network, Complex & Intelligent Systems, 7(4), 2023-2036.
Sankaran, K.S.; Thangapandian, M.; Vasudevan, N. (2021). Brain tumor grade identification using deep Elman neural network with adaptive fuzzy clustering-based segmentation approach, Multimedia Tools and Applications, 80(16), 25139-25169.
Lin, M.; Momin, S.; Lei, Y.; Wang, H.; Curran, W.J.; Liu, T.; Yang, X. (2021). Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net, Medical Physics, 48(8), 4365-4374.
Ilhan, A.; Sekeroglu, B.; Abiyev, R. (2022). Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net, International Journal of Computer Assisted Radiology and Surgery, 17(3), 589-600.
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