A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth inc...

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Vydáno v:	Journal of medical systems Ročník 43; číslo 11; s. 326
Hlavní autoři:	Amin, Javeria, Sharif, Muhammad, Yasmin, Mussarat, Saba, Tanzila, Anjum, Muhammad Almas, Fernandes, Steven Lawrence
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2019 Springer Nature B.V
Témata:	Algorithms Brain cancer Brain damage Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumors Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image classification Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Ischemia Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neural Networks, Computer Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Tomography, X-Ray Computed - methods Transfer learning Tumor cells Tumors Segmentation Alex network Fused score vector Classification Google network Brain tumor detection Softmax layer
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.
AbstractList	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively. Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.
ArticleNumber	326
Author	Fernandes, Steven Lawrence Saba, Tanzila Amin, Javeria Yasmin, Mussarat Anjum, Muhammad Almas Sharif, Muhammad
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Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
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Sharif, M., Khan, M.A., Faisal, M., Yasmin, M., and Fernandes, S.L., A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters, 2018. Cheng, G., Zhou, P., and Han, JJIToIP., Duplex metric learning for image set classification. 27 (1):281–292, 2018. Raja, N.S.M., Fernandes, S., Dey, N., Satapathy, S.C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing:1–12, 2018. Lee, C.-H., Wang, S., Murtha, A., Brown, M.R., and Greiner, R., Segmenting brain tumors using pseudo–conditional random fields. International conference on medical image computing and computer-assisted intervention. Springer. 359–366, 2008. MaierOMenzeBHvon der GablentzJHäniLHeinrichMPLiebrandMWinzeckSBasitABentleyPChenLISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRIMed. Image Analy.20173525026910.1016/j.media.2016.07.009 ZhaoXWuYSongGLiZZhangYFanYA deep learning model integrating FCNNs and CRFs for brain tumor segmentationMed. Image Analy.2018439811110.1016/j.media.2017.10.002 Adams, R., and Bischof, L.J., ITopa, intelligence m. Seeded region growing. 16 (6):641–647, 1994. Rajinikanth, V., Fernandes, S.L., Bhushan, B., and Sunder, N.R., Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. 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Bioinform.20191443053131:CAS:528:DC%2BC1MXhtVGhsb3J10.2174/1574893614666181220094918 – reference: Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., and Oliva, A., Places: An image database for deep scene understanding, (2016). – reference: Simon Andermatt, S.P., and Cattin, P., Multi-dimensional gated recurrent units for brain tumor segmentation. Proceedings of the 6th MICCAI BraTS Challenge (2017), 1984. – reference: Amorim, P.H.A.C.V.S., Escudero, G.G., Oliveira, D.D.C., Pereira, S.M., Santos, H.M., and Scussel, A.A., 3D U-nets for brain tumor segmentation in MICCAI 2017 BraTS challenge proceedings of the 6th MICCAI BraTS Challenge, 2017. – reference: Raja, N.S.M., Fernandes, S., Dey, N., Satapathy, S.C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. 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Snippet	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain....
SourceID	proquest pubmed crossref springer
SourceType	Aggregation Database Index Database Enrichment Source Publisher
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SubjectTerms	Algorithms Brain cancer Brain damage Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumors Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image classification Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Ischemia Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neural Networks, Computer Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Tomography, X-Ray Computed - methods Transfer learning Tumor cells Tumors
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Title	A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning
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