Skip to main content
SpringerLink
Log in

Classification Methods for Pathological Brain Detection

  • Chapter
  • First Online:
Pathological Brain Detection

Part of the book series: Brain Informatics and Health ((BIH))

Abstract

In PBD systems, classification entails identifying which disease category a new magnetic resonance (MR) image belongs to. This chapter first describes four pre-design tasks: the trade-off between bias and variance, data volume and classifier complexity, noise at the target, and the class imbalance problem. Further to this, three canonical classifiers: the naive Bayesian classifier, the decision tree (trained by ID3 and C4.5), and k-nearest neighbors, are discussed. Two variants of this last classifier are analyzed: one-nearest neighbor and weighted nearest neighbor. The support vector machine is extremely important in both PBD and other applications. Its mathematical fundamentals are analyzed. The generalized eigenvalue proximal support vector machine, twin support vector machine, and fuzzy support vector machine are expatiated. The multiclass technique to generalize the support vector machine for multiclass problems is provided.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Torgo L, Branco P, Ribeiro RP, Pfahringer B (2015) Resampling strategies for regression. Expert Syst 32(3):465–476. https://doi.org/10.1111/exsy.12081

    Article  Google Scholar 

  2. Punitha K, Latha B (2016) Sampling imbalance dataset for software defect prediction using hybrid neuro-fuzzy systems with naive bayes classifier. Tehnicki Vjesnik-Tech Gaz 23(6):1795–1804. https://doi.org/10.17559/tv-20151219112129

    Article  Google Scholar 

  3. Swetapadma A, Yadav A (2016) Protection of parallel transmission lines including inter-circuit faults using Naive Bayes classifier. Alexandria Eng J 55(2):1411–1419. https://doi.org/10.1016/j.aej.2016.03.029

    Article  Google Scholar 

  4. Rahmatian M, Chen YC, Palizban A, Moshref A, Dunford WG (2017) Transient stability assessment via decision trees and multivariate adaptive regression splines. Electr Power Syst Res 142:320–328. https://doi.org/10.1016/j.epsr.2016.09.030

    Article  Google Scholar 

  5. Sathyadevan S, Nair RR (2015) Comparative analysis of decision tree algorithms: ID3, C4.5 and random forest. In: Jain LC, Behera HS, Mandal JK, Mohapatra DP (eds) Computational intelligence in data mining. Smart innovation systems and technologies, vol 31. Springer, Berlin, pp 549–562. https://doi.org/10.1007/978-81-322-2205-7_51

  6. Zimmerman RK, Balasubramani GK, Nowalk MP, Eng H, Urbanski L, Jackson ML, Jackson LA, McLean HQ, Belongia EA, Monto AS, Malosh RE, Gaglani M, Clipper L, Flannery B, Wisniewski SR (2016) Classification and regression tree (CART) analysis to predict influenza in primary care patients. BMC Infect Dis 16, Article ID: 503. https://doi.org/10.1186/s12879-016-1839-x

  7. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1

    Article  Google Scholar 

  8. McRoberts RE, Domke GM, Chen Q, Naesset E, Gobakken T (2016) Using genetic algorithms to optimize k-Nearest neighbors configurations for use with airborne laser scanning data. Remote Sens Environ 184:387–395. https://doi.org/10.1016/j.rse.2016.07.007

    Article  Google Scholar 

  9. Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32(4):631–644. https://doi.org/10.1007/s00366-016-0442-5

    Article  Google Scholar 

  10. Chon AT (2010) Design of Lazy Classifier based on Fuzzy k-Nearest Neighbors and Reconstruction Error (퍼지 k-Nearest Neighbors 와 Reconstruction Error 기반 Lazy Classifier 설계). J Korean Inst Intell Syst 20(1):101–108

    Article  Google Scholar 

  11. Zhang LA, Parker RS, Swigon D, Banerjee I, Bahrami S, Redl H, Clermont G (2016) A one-nearest-neighbor approach to identify the original time of infection using censored baboon sepsis data. Crit Care Med 44(6):E432–E442. https://doi.org/10.1097/ccm.0000000000001623

    Article  Google Scholar 

  12. Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74. https://doi.org/10.1109/tpami.2006.17

    Article  Google Scholar 

  13. Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4):1795–1813. https://doi.org/10.3390/e17041795

    Article  Google Scholar 

  14. Jayadeva, Khemchandani R., Chandra S. (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910. https://doi.org/10.1109/tpami.2007.1068

  15. Yang M (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6), Article ID: 169

    Google Scholar 

  16. Yadav AK, Mehta R, Kumar R, Vishwakarma VP (2016) Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking. Multimedia Tools Appl 75(15):9371–9394. https://doi.org/10.1007/s11042-016-3381-7

    Article  Google Scholar 

  17. Chen S, Yang J-F, Phillips P (2015) Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol 25(4):317–327. https://doi.org/10.1002/ima.22144

    Article  Google Scholar 

  18. Lu HM (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4:8375–8385. https://doi.org/10.1109/ACCESS.2016.2628407

    Article  Google Scholar 

  19. Kuri-Morales A, Mejia-Guevara I (2006) Evolutionary training of SVM for multiple category classification problems with self-adaptive parameters. In: Sichman JS, Coelho H, Rezende SO (eds) 10th Ibero-American conference on artificial intelligence/18th Brazilian symposium on artificial intelligence, Riberiao Preto, Brazil. Lecture Notes in computer science. Springer, pp 329–338

    Google Scholar 

  20. Cholissodin I, Kurniawati M, Indriati, Arwani I (2014) Classification of campus e-complaint documents using directed acyclic graph multi-class SVM based on analytic hierarchy process. In: International conference on advanced computer science and information system, Jakarta, Indonesia. IEEE, pp 247–253. https://doi.org/10.1109/icacsis.2014.7065835

  21. Gorriz JM, Ramírez J (2016) Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci 10, Article ID: 160. https://doi.org/10.3389/fncom.2016.00106

  22. King RTFA, Tu X, Dessaint LA, Kamwa I (2016) Multi-contingency transient stability-constrained optimal power flow using multilayer feedforward neural networks. In: Canadian conference on electrical and computer engineering (CCECE), Canada. IEEE, pp 1–6. https://doi.org/10.1109/ccece.2016.7726774

  23. Dolezel P, Skrabanek P, Gago L (2016) Detection of grapes in natural environment using feedforward neural network as a classifier. In: SAI computing conference, London, UK. IEEE, pp 1330–1334. https://doi.org/10.1109/sai.2016.7556153

  24. Njikam ANS, Zhao H (2016) A novel activation function for multilayer feed-forward neural networks. Appl Intell 45(1):75–82. https://doi.org/10.1007/s10489-015-0744-0

    Article  Google Scholar 

  25. Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24(11):2673–2688. https://doi.org/10.1007/s11269-009-9573-4

    Article  Google Scholar 

  26. Liao B, Xu JG, Lv JT, Zhou SL (2015) An image retrieval method for binary images based on DBN and softmax classifier. IETE Tech Rev 32(4):294–303. https://doi.org/10.1080/02564602.2015.1015631

    Article  Google Scholar 

  27. Hara K, Saito D, Shouno H (2015) Analysis of function of rectified linear unit used in deep learning. In: International joint conference on neural networks, Killarney, Ireland, IEEE international joint conference on neural networks (IJCNN). IEEE, pp 144–151

    Google Scholar 

  28. Al-Yaseen WL, Othman ZA, Nazri MZA (2017) Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst Appl 67:296–303. https://doi.org/10.1016/j.eswa.2016.09.041

    Article  Google Scholar 

  29. Sokolov-Mladenovic S, Milovancevic M, Mladenovic I, Alizamir M (2016) Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Comput Hum Behav 65:43–45. https://doi.org/10.1016/j.chb.2016.08.014

    Article  Google Scholar 

  30. Sungheetha A, Sharma RR (2016) Extreme learning machine and fuzzy K-nearest neighbour based hybrid gene selection technique for cancer classification. J Med Imaging Health Inform 6(7):1652–1656. https://doi.org/10.1166/jmihi.2016.1866

    Article  Google Scholar 

  31. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Networks 17(6):1411–1423. https://doi.org/10.1109/tnn.2006.880583

    Article  Google Scholar 

  32. Meruane V (2016) Online sequential extreme learning machine for vibration-based damage assessment using transmissibility data. J Comput Civil Eng 30(3), Article ID: 04015042. https://doi.org/10.1061/(asce)cp.1943-5487.0000517

  33. Ghimire D, Lee J (2016) Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection. Multimedia Tools Appl 75(18):11181–11197. https://doi.org/10.1007/s11042-015-2839-3

    Article  Google Scholar 

  34. Wang LPP, Wan CRR (2008) Comments on “The Extreme Learning Machine.”. IEEE Trans Neural Networks 19(8):1494–1495. https://doi.org/10.1109/tnn.2008.2002273

    Article  Google Scholar 

  35. Li MN, Kwak KC, Kim YT (2016) Estimation of energy expenditure using a patch-type sensor module with an incremental radial basis function neural network. Sensors 16(10), Article ID: 1566. https://doi.org/10.3390/s16101566

  36. Mateo-Sotos J, Torres AM, Sanchez-Morla EV, Santos JL (2016) An adaptive radial basis function neural network filter for noise reduction in biomedical recordings. Circ Syst Sig Process 35(12):4463–4485. https://doi.org/10.1007/s00034-016-0281-z

    Article  MathSciNet  MATH  Google Scholar 

  37. Lu Z (2016) A pathological brain detection system based on radial basis function neural network. J Med Imaging Health Inform 6(5):1218–1222

    Article  Google Scholar 

  38. Nagamani G, Radhika T (2015) Dissipativity and passivity analysis of T-S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach. Nonlinear Dyn 82(3):1325–1341. https://doi.org/10.1007/s11071-015-2241-8

    Article  MathSciNet  MATH  Google Scholar 

  39. Naggaz N, Wei G (2009) Remote-sensing image classification based on an improved probabilistic neural network. Sensors 9(9):7516–7539

    Article  Google Scholar 

  40. Padil KH, Bakhary N, Hao H (2017) The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection. Mech Syst Signal Process 83:194–209. https://doi.org/10.1016/j.ymssp.2016.06.007

    Article  Google Scholar 

  41. Chen Y, Zhang Y, Lu H (2016) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimedia Tools Appl. https://doi.org/10.1007/s11042-016-4161-0

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China

    Shui-Hua Wang & Yu-Dong Zhang

  2. School of Electronics Science and Engineering, Nanjing University, Nanjing, Jiangsu, China

    Shui-Hua Wang

  3. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  4. Translational Imaging Division & MRI Unit, New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, USA

    Zhengchao Dong

  5. Shepherd University, Shepherdstown, WV, USA

    Preetha Phillips

  6. West Virginia School of Osteopathic Medicine, St. Lewisburg, WV, USA

    Preetha Phillips

Authors
  1. Shui-Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengchao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Preetha Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, SH., Zhang, YD., Dong, Z., Phillips, P. (2018). Classification Methods for Pathological Brain Detection. In: Pathological Brain Detection. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4026-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4026-9_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4025-2

  • Online ISBN: 978-981-10-4026-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation