Brain tumor classification based on hybrid approach
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ORIGINAL ARTICLE
Brain tumor classification based on hybrid approach Wadhah Ayadi1
· Imen Charfi2 · Wajdi Elhamzi3
· Mohamed Atri4
Accepted: 17 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Various computer systems have attracted more researchers’ attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method. Keywords MRI · Classification · Brain tumor · DSURF · HoG
1 Introduction The brain is very complex and sensitive, and it controlled the overall body functionality. It can be affected by a tumor. The tumor can make changes in brain behavior and structure. So, any brain damage affects the body badly. Brain tumor represents a group of abnormal cells where they are produced from parenchyma in the brain or in neighboring parts. Brain cancer can conduct to extreme disabilities
B
Wajdi Elhamzi [email protected] Wadhah Ayadi [email protected] Imen Charfi [email protected] Mohamed Atri [email protected]
1
Laboratory of Electronics and Microelectronics, University of Monastir, Monastir 5000, Tunisia
2
Higher School of Sciences and Technology of Hammam Sousse, University of Sousse, Sousse, Tunisia
3
College of Computer Sciences and Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
4
College of Computer Science, King Khalid University, Abha, Saudi Arabia
that significantly hold back to patient’s activities [1]. Brain tumor is considered through the main causes of the increase in mortality in the whole world, which augment the burden for hospitals and society. Recently, many comprehensive statistical studies concerning brain cancer in the world are published [2–4]. The detected cases of brain cancer in the USA are increased from 1.5 million to 1.658 million between 2013 and 2015 [1]. In 2018, the approximated number of deaths is near 16,830 [5]. In the aim to generate good brain image without using radioactive traces or ionizing radiation, MRI is exploited. It provides
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