Brain tumor detection based on extreme learning
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S . I . : R E C E N T A D V A N C E S I N D E E P L E A R N I N G F O R M E D I C A L I M A G E P R O CE S S I N G
Brain tumor detection based on extreme learning Muhammad Sharif1 • Javaria Amin1,2 • Mudassar Raza1 Shafqat Ali Shad5
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Muhammad Almas Anjum3 • Humaira Afzal4
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Received: 17 December 2018 / Accepted: 10 December 2019 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Gliomas are dreadful and common type of brain tumor. Therefore, treatment planning is significant to increase the survival rate of gliomas patients. The large structural and spatial variation between tumors makes an automated detection more challenging. Brain magnetic resonance imaging is utilized for tumor evaluation on the basis of automated segmentation and classification methods. In this work, triangular fuzzy median filtering is applied for image enhancement that helps in accurate segmentation based on unsupervised fuzzy set method. Gabor features are extracted across each candidate’s lesions, and similar texture (ST) features are calculated. These ST features are supplied to extreme learning machine (ELM), and regression ELM leaves one out for tumor classification. The technique is evaluated on BRATS 2012, 2013, 2014 and 2015 challenging datasets as well as on 2013 Leader board. The proposed approach shows better results and less computational time. Keywords Fuzzy rules Erosion MRI Dilation Gabor filter Gliomas
1 Introduction Brain tumor in humans is an unrestrained development of cells [1–6]. It has two types which are malignant or benign. The benign tumors are homogeneous structures in which cancerous cells are not included, while malignant tumor has cancerous cells. American Brain Tumors Association [7] and World Health Organization (WHO) has introduced tumor grading mechanism such that I and II grades are known as benign, while III and IV grades are classified into malignant. The growth rate of benign is slow as compared
& Mudassar Raza [email protected] 1
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment, Pakistan
2
Department of Computer Science, University of Wah, Wah Cantonment, Pakistan
3
National University of Sciences & Technology, College of Electrical and Mechanical Engineering, Rawalpindi, Pakistan
4
Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan
5
Department of Computer Science, Luther College, Decorah, IA, USA
to malignant. When low-grade tumor is not treated timely, it gets converted into high-grade tumor [8]. Therefore, brain tumor diagnosis is a primary objective for radiologists [9]. Grade II gliomas patients need regular treatment and monitoring through MRI [10] because it is more helpful to validate gliomas in clinical practice. MRI gives a detailed structure of human brain without using any ionization radiation. This modality is considered good for brain tumor segmentation, but it is difficult to completely segment and classify healthy/abno
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