ANALYSIS ON DEEP LEARNING TO DETECT THE BRAIN MRI TUMOR SEGEMENTATION
Keywords:
Brain tumour, Local feature, Image classification, Segmentation, Residual.Abstract
In this recent scenario, brain tumour detection system through classification technique helps in the person infected with brain tumour and plays an important role to provide effectiveness in the diagnosis process and proper treatment. The above MRI classification method helps to provide effective and early-stage treatment for identifying the tumour in the brain and determine the level of tumour occurrences. As there are several classification methods related to brain tumour exists in recent days, which is associated with U-Net architecture i.e., deep learning methods related to medical classification. Based on the comparison of the feature information among low-level and high-level, in this paper the propose architecture of Residual U-NET with some enhanced local feature information as it helps to improve the medical image segmentation. Through this, propose work helps to highlight the improvement in the attention module for the segmentation of image tumour, propose the novel attention module based on the Residual U-Net model. In the modified Residual U-Net model, residual module and attention gate is addressed along with dropout and wide context layers. Here the addition of salient feature information, which helps to focus on the large sensitive scaled information but also consider the small-scale images also. In the performance analysis, modified model associated with gate attention outperforms with the existing models like, U-Net and CNN Densely models.
References
. IramShahzadi; Tong Boon Tang; Fabrice Meriadeau; Abdul Quyyum, “CNN-LSTM: Cascaded Framework for Brain Tumour Classification,” in Proc. Of IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2018.
. J Salo, A Niemelä, M Joukamaa, J Koivukangas, “Effect of brain tumour laterality on patients' perceived quality of life,” Journal of Neurology, Neurosurgery & Psychiatry, BMJ Journals, Vol. 72, Issue. 3, 2002.
. Abhishta Bhandari, Jarrad Koppen1 and Marc Agzarian, “Convolutional neural networks for brain tumour segmentation,” Insight into Image, Springer, Vol. 11, Issue. 77, 2020.
. Meiyu Li, Hailiang Tang, Michael D. Chan, Xiaobo Zhou, Xiaohua Qian, “DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet,” Medical Physics: International Journal of Medical Physics Research and Practice, Vol.47, Issue. 3, pp. 1139-1150, 2020.
. Hassan Ali Khan, Wu Jue1, Muhammad Mushtaq and Muhammad UmerMushtaq, “Brain tumour classification in MRI image using convolutional neural network,” AIMS: Mathematical Bioscienceand Engineering, Vol. 15, Issue. 5, pp. 6203-6216, 2020.
. R Lokesh Kumar, Jagadeesh Kakarla, B VenkateswarluIsunuri&Munesh Singh, “Multi-class brain tumour classification using residual network and global average pooling,” Multimedia Tools and Applications, Cluster Computing, Vol. 80, pp. 13429–13438, 2021.
. AbtinRiasatian, MortezaBabaie, Danial Maleki, ShivamKalra, MojtabaValipour, SobhanHemati, ManitZaveri, Amir Safarpoor, SobhanShafiei, Mehdi Afshari, MaralRasoolijaberi, MiladSikaroudi, Mohd Adnan, Sultaan Shah, Charles Choi, SavvasDamaskinos, Clinton JV Campbell, PhediasDiamandis, LironPantanowitz, Hany Kashani, Ali Ghodsi, H.R.Tizhoosh, “Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides,” Medical Image Analysis, Elsevier Publications, Vol. 70, 2021.
. A.Rajendran and R.Dhanasekaran, “Fuzzy Clustering and Deformable Model for Tumour Segmentation on MRI Brain Image: A Combined Approach,” Procedia Engineering, Elsevier Publications, Vol. 30, pp. 327-333, 2012.
. Pawel Mlynarski, HervéDelingette, Antonio Criminisi, Nicholas Ayache, “Deep learning with mixed supervision for brain tumour segmentation,” Journal of Medical Imaging, Vol. 6, Issue. 3, 2019.
. M. Mohammed Thaha, K. Pradeep Mohan Kumar, B. S. Murugan, S. Dhanasekeran, P. Vijayakarthick& A. SenthilSelvi, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” Image and Signal Processing, Springer, Vol. 43, 2019.
. Pablo Ribalta Lorenzo, Jakub Nalepa, Barbara Bobek-Billewicz, Pawel Wawrzyniak, GrzegorzMrukwa, Michal Kawulok, Pawel Ulrych, Michael P.Hayball, “Segmenting brain tumors from FLAIR MRI using fully convolutional neural networks,” Computer Methods and Programs in Biomedicine, Elsevier Publications, Vol. 176, pp. 135 – 148, 2019.
. Manda SSSNMSRL Pavan and P. Jagadeesh, “Brain Tumor Segmentation Using Covolutional Neural Network In MRI Images,” International Journal of Pure and Applied Mathematics, Vol. 119, No. 17, pp. 1585 – 1592, 2018.
. IramShahzadi; Tong Boon Tang; Fabrice Meriadeau; Abdul Quyyum, “CNN-LSTM: Cascaded Framework for Brain Tumour Classification,” In Proc. Of IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2018.
. MohammadrezaSoltaninejad, Lei Zhang, TryphonLambrou, Guang Yang, Nigel Allinson, Xujiong Ye, “MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks,” International MICCAI Brainlesion Workshop BrainLes: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 204 – 215, 2017.
. Asra Rafi, Tahir Mustafa Madni, Uzair Iqbal Janjua, Muhammad Junaid Ali, Muhammad NaeemAbid, “Multi-level dilated convolutional neural network for brain tumour segmentation and multi-view-based radiomics for overall survival prediction,” International Journal of Imaging Systems and Technology, Wiley, 2021.
. Zekun Wang; Yanni Zou; Peter X. Liu; “Hybrid dilation and attention residual U-Net for medical image segmentation,” Computers in Biology and medicine,” Vol. 134, 2021.
. Mobarakol Islam, V. S. Vibashan, V. Jeya Maria Jose, NavodiniWijethilake, Uppal Utkarsh, Hongliang Ren, “Brain Tumor Segmentation and Survival Prediction Using 3D Attention UNet,” International MICCAI Brainlesion Workshop BrainLes: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 262 – 272, 2019.
. GökayKarayegen, Mehmet FeyziAksahin, “Brain tumour prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumour region,” Biomedical Signal Processing and Control, Elsevier Publications, 2021.
. WorkuJifaraSori, Jiang Feng &Shaohui Liu, “Multi-path convolutional neural network for lung cancer detection,” Multidimensional Systems and Signal Processing, Springer Publications, Vol. 30, pp. 1749 – 1768, 2019.
. Jianxin Zhang, XiaogangLv, Hengbo Zhang and Bin Liu, “AResU-Net: Attention Residual U- Net for Brain Tumour Segmentation,” Symmetry, Mdpi, Vol. 12, Issue. 5, 2020.
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