Abstract
In this chapter, three data sets for single-slice pathological brain detection (PBD), along with their download URLs, are given. All the data sets can be downloaded from The Whole Brain Atlas from the Harvard Medical School. The inclusion criteria of three commonly used data sets are introduced. The limitation of using didactic images is explained. In the field of pattern recognition, a training set is necessary, where data are labelled using known categories. The validation set is important to optimize hyper-parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patnaik LM, Chaplot S, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92. https://doi.org/10.1016/j.bspc.2006.05.002
El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process 20(2):433–441. https://doi.org/10.1016/j.dsp.2009.07.002
Wu L (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053
Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagnet Res 130:369–388
Saritha M, Joseph KP, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156. https://doi.org/10.1016/j.patrec.2013.08.017
Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of Ripplet. Prog Electromagnet Res-Pier 137:1–17. https://doi.org/10.2528/pier13010105
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
Zhou X, Xu W, Sun P (2015) Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. In: Ortuño F, Rojas I (eds) Bioinformatics and biomedical engineering, Granada, Spain lecture notes in computer science. Springer International Publishing, pp 201–209. https://doi.org/10.1007/978-3-319-16483-0_20
Feng C (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164. https://doi.org/10.1002/ima.22132
Phillips P, Dong Z, Yang J (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagnet Res 152:41–58. https://doi.org/10.2528/PIER15040602
Sun P (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med Mater Eng 26(s1):1283–1290. https://doi.org/10.2528/PIER13121310
Liu A (2015) Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J Med Imaging Health Inform 5(7):1395–1403. https://doi.org/10.1166/jmihi.2015.1542
Liu G (2015) Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 4(1), Article ID: 716
Yang X, Sun P, Dong Z, Liu A, Yuan T-F (2015) Pathological brain detection by a novel image feature—fractional fourier entropy. Entropy 17(12):8278–8296. https://doi.org/10.3390/e17127877
Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188–197. https://doi.org/10.1016/j.neucom.2015.11.034
Atangana A (2016) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-3401-7
Zhou X-X, Yang J-F, Sheng H, Wei L, Yan J, Sun P (2016) Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation 92(9):827–837. https://doi.org/10.1177/0037549716629227
Yang M (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6), Article ID: 169
Yang JF, Sun P (2016) Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. Biomed Eng Biomed Tech 61(4):431–441. https://doi.org/10.1515/bmt-2015-0152
Sun Y (2016) A multilayer perceptron based smart pathological brain detection system by fractional fourier entropy. J Med Syst 40(7), Article ID: 173. https://doi.org/10.1007/s10916-016-0525-2
Chen X-Q (2016) Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access 4:5937–5947. https://doi.org/10.1109/ACCESS.2016.2611530
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wang, SH., Zhang, YD., Dong, Z., Phillips, P. (2018). Comparison of Artificial Intelligence–Based Pathological Brain Detection Systems. In: Pathological Brain Detection. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4026-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-4026-9_10
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)