Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm
- 257 Downloads
Pathological brain detection is an automated computer-aided diagnosis for brain images. This study provides a novel method to achieve this goal.We first used synthetic minority oversampling to balance the dataset. Then, our system was based on three components: wavelet packet Tsallis entropy, extreme learning machine, and Jaya algorithm. The 10 repetitions of K-fold cross validation showed our method achieved perfect classification on two small datasets, and achieved a sensitivity of 99.64 ± 0.52%, a specificity of 99.14 ± 1.93%, and an accuracy of 99.57 ± 0.57% over a 255-image dataset. Our method performs better than six state-of-the-art approaches. Besides, Jaya algorithm performs better than genetic algorithm, particle swarm optimization, and bat algorithm as ELM training method.
KeywordsPathological brain detection Synthetic minority oversampling Extreme learning machine Jaya algorithm
The paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601).
Y Zhang & G Zhao conceived the study. J Sun & T Zhan designed the model. Y Zhang & J Li acquired the data. Y Zhang, T Zhan, J Li analyzed the data. G Zhao interpreted the data. Y Zhang, X Wu, Z Wang, H Liu, V Govindaraj developed the programs. Y Zhang & T Zhan wrote the draft. All the authors gave critical revisions and approved the submission.
Compliance with ethical standards
Conflict of interest
We declared there is no conflict of interest in terms with this submission.
- 1.Chen Y (2016) Voxelwise detection of cerebral microbleed in CADASIL patients by leaky rectified linear unit and early stopping: A class-imbalanced susceptibility-weighted imaging data study. Multimed Tools Appl. doi: 10.1007/s11042-017-4383-9 (Online)
- 3.Chen H (2017) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimed Tools Appl. doi: 10.1007/s11042-017-4554-8 (Online)
- 7.Doreswamy, Salma MU (2015) BAT-ELM: a bio inspired model for prediction of breast cancer data. In: International Conference on Applied and Theoretical Computing And Communication Technology (Icatcct). Davangere, IEEE, pp. 501–506Google Scholar
- 10.Huo Y, Carass A, Resnick SM et al (2016) Combining Multi-atlas Segmentation with Brain Surface Estimation. In: Conference on Medical Imaging - Image Processing. San Diego, Spie-Int Soc Optical Engineering, p 97840EGoogle Scholar
- 12.Jiang Y, Zhu W (2017) Exploring a smart pathological brain detection method on pseudo Zernike moment. Multimed Tools Appl. doi: 10.1007/s11042-017-4703-0 (Online)
- 17.Mustafa N, Memon RA, Li JP et al (2017) A Classification Model for Imbalanced Medical Data based on PCA and Farther Distance based Synthetic Minority Oversampling Technique. Int J Adv Comput Sci Appl 8(1):61–67Google Scholar
- 24.Sun P (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Biomed Mater Eng 26(s1):1283–1290Google Scholar
- 27.Wang H, Lv Y (2016) Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimed Tools Appl. doi: 10.1007/s11042-016-4242-0 (Online)
- 31.Yang J (2015) Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17(10):6663–6682Google Scholar
- 33.Ying ZB, Li H, Ma JF et al (2016) Adaptively secure ciphertext-policy attribute-based encryption with dynamic policy updating. Science China-Information Sciences 59(4):16, 042701Google Scholar
- 34.Yuan TF (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 9:66Google Scholar