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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Shrnutí: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.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1453-8