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A comprehensive study of brain tumour discrimination using phase combinations, feature rankings, and hybridised classifiers

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The binary categorisation of brain tumours is challenging owing to the complexities of tumours. These challenges arise because of the diversities between shape, size, and intensity features for identical types of tumours. Accordingly, framework designs should be optimised for two phenomena: feature analyses and classification. Based on the challenges and difficulty of the issue, limited information or studies exist that consider the binary classification of three-dimensional (3D) brain tumours. In this paper, the discrimination of high-grade glioma (HGG) and low-grade glioma (LGG) is accomplished by designing various frameworks based on 3D magnetic resonance imaging (3D MRI) data. Accordingly, diverse phase combinations, feature-ranking approaches, and hybrid classifiers are integrated. Feature analyses are performed to achieve remarkable performance using first-order statistics (FOS) by examining different phase combinations near the usage of single phases (T1c, FLAIR, T1, and T2) and by considering five feature-ranking approaches (Bhattacharyya, Entropy, Roc, t test, and Wilcoxon) to detect the appropriate input to the classifier. Hybrid classifiers based on neural networks (NN) are considered due to their robustness and superiority with medical pattern classification. In this study, state-of-the-art optimisation methods are used to form the hybrid classifiers: dynamic weight particle swarm optimisation (DW-PSO), chaotic dynamic weight particle swarm optimisation (CDW-PSO), and Gauss-map-based chaotic particle-swarm optimisation (GM-CPSO). The integrated frameworks, including DW-PSO-NN, CDW-PSO-NN, and GM-CPSO-NN, are evaluated on the BraTS 2017 challenge dataset involving 210 HGG and 75 LGG samples. The 2-fold cross-validation test method and seven metrics (accuracy, AUC, sensitivity, specificity, g-mean, precision, f-measure) are processed to evaluate the performance of frameworks efficiently. In experiments, the most effective framework is provided that uses FOS, data including three phase combinations, the Wilcoxon feature-ranking approach, and the GM-CPSO-NN method. Consequently, our framework achieved remarkable scores of 90.18% (accuracy), 85.62% (AUC), 95.24% (sensitivity), 76% (specificity), 85.08% (g-mean), 91.74% (precision), and 93.46% (f-measure) for HGG/LGG discrimination of 3D brain MRI data.

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  1. Li H, Li A, Wang M (2019) A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Comput Biol Med 108:150–160

    Article  Google Scholar 

  2. Li Y, Jia F, Qin J (2016) Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artif Intell Med 73:1–13

    Article  Google Scholar 

  3. Soltaninejad M, Yang G, Lambrou T et al (2017) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Ass Rad 12(2):183–203

    Google Scholar 

  4. Ural B (2018) A computer-based brain tumor detection approach with advanced image processing and probabilistic neural network methods. J Med Biol Eng 38(6):867–879

    Article  Google Scholar 

  5. Angulakshmi M, Priya GL (2019) Walsh Hadamard transform for simple linear iterative clustering (SLIC) superpixel based spectral clustering of multimodal MRI brain tumor segmentation. IRBM 40(5):253–262

    Article  Google Scholar 

  6. Amarapur B (2018) Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier. Multimed Tools Appl:1–29

  7. Wu Y, Liu B, Wu W, Lin Y, Yang C, Wang M (2018) Grading glioma by radiomics with feature selection based on mutual information. J Ambient Intell Humaniz Comput 9(5):1671–1682

    Article  Google Scholar 

  8. Ahmed HM, Youssef BA, Elkorany AS, Elsharkawy ZF, Saleeb AA, El-Samie FA (2019) Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine. Multimed Tools Appl:27983–28002

  9. Koyuncu H, Ceylan R, Asoglu S, Cebeci H, Koplay M (2019) An extensive study for binary characterisation of adrenal tumours. Med Biol Eng Comput 57(4):849–862

    Article  Google Scholar 

  10. Koyuncu H (2020) GM-CPSO: a new viewpoint to chaotic particle swarm optimization via Gauss map. Neural Process Lett 52:241–266

    Article  Google Scholar 

  11. Koyuncu H (2019) Parkinson’s disease recognition using Gauss map based chaotic particle swarm – neural network. In: Proceedings of 2019 IEEE 6th International Conference Engineering and Telecomunication (En&T 2019), 2019, pp 1–4

  12. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation, (1998), pp 69–73

    Google Scholar 

  13. Koyuncu H, Ceylan R (2018) A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems. J Comput Des Eng 6(2):129–142

    Google Scholar 

  14. Chen K, Zhou F, Liu A (2018) Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowl-Based Syst 139:23–40

    Article  Google Scholar 

  15. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Applic 25(5):1077–1097

    Article  Google Scholar 

  16. Ceylan R, Koyuncu H (2016) A new breakpoint in hybrid particle swarm-neural network architecture: Individual boundary adjustment. Int J Inf Tech Decis 15(6):1313–1343

    Article  Google Scholar 

  17. Chu C, Hsu AL, Chou KH, Bandettini P, Lin C (2012) Alzheimer’s disease neuroimaging initiative: does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1):59–70

    Article  Google Scholar 

  18. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244

    Article  Google Scholar 

  19. Pourreza A, Lee WSD, Raveh E, Hong Y, Kim HJ (2013) Identification of citrus greening disease using a visible band image analysis. In: American Society of Agricultural and Biological Engineers, vol 2013, p 1

    Google Scholar 

  20. Nguyen T, Nahavandi S, Creighton D, Khosravi A (2015) Mass spectrometry cancer data classification using wavelets and genetic algorithm. FEBS Lett 589(24PartB):3879–3886

    Article  CAS  Google Scholar 

  21. Vakharia V, Gupta VK, Kankar PK (2016) A comparison of feature ranking techniques for fault diagnosis of ball bearing. Soft Comput 20(4):1601–1619

    Article  Google Scholar 

  22. Materka A, Strzelecki M (1998) Texture analysis methods–a review. Technical university of lodz, institute of electronics, COST B11 report, Brussels, 9–11

  23. Ceylan R, Koyuncu H (2019) A novel rotation forest modality based on hybrid NNs: RF (ScPSO-NN). J King Saud Uni Comput Inform Sci 31(2):235–251

    Google Scholar 

  24. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  Google Scholar 

  25. Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE T Med Imaging 35(5):1196–1206

    Article  Google Scholar 

  26. Al-antari MA, Al-masni MA, Park SU, Park J, Metwally MK, Kadah YM, Han SM, Kim TS (2018) An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng 38(3):443–456

    Article  Google Scholar 

  27. Jain PK, Gupta S, Bhavsar A, Nigam A, Sharma N (2020) Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach. Med Biol Eng Comput:1–12

  28. Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE T Med Imaging 34(10):1993–2024

    Article  Google Scholar 

  29. Bakas S, Akbari H, Sotiras A et al (2017) Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117

    Article  Google Scholar 

  30. Bakas S, Reyes M, Jakab A et al (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint:1811.02629

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This work is supported by the Coordinatorship of Konya Technical University’s Scientific Research Projects.

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Correspondence to Hasan Koyuncu.

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This paper contributes to the literature as follows:

• The design of a task-specific classification (HGG/LGG discrimination) framework to apply directly after the segmentation section of a CAD system

• A study presenting detailed phase combination and feature-ranking evaluations for the design of a specific framework

• A detailed study handling the comparison of optimised NNs on framework design for brain tumour categorisation on 3D MRI data

• An extensive study about the binary classification of 3D brain tumours

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Koyuncu, H., Barstuğan, M. & Öziç, M.Ü. A comprehensive study of brain tumour discrimination using phase combinations, feature rankings, and hybridised classifiers. Med Biol Eng Comput 58, 2971–2987 (2020).

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