Abstract
The classification of histopathological images is a challenging task in the study of real-life medicine owing to the diverse geometrical structures and different histology features. This paper proposes a framework called Local Receptive Field based Extreme Learning Machine with Three Channels (3C-LRF-ELM), which can automatically extract histopathological features to diagnose whether there is a disease. We conduct experiments on the real-world image dataset that consists of mammalian lung, kidney and spleen organ images provided by the animal diagnostics lab (ADL) Pennsylvania State University. The training sets are consisted of overlapping blocks which are randomly extracted from arbitrary 40 images of each type image in the ADL dataset. The remaining images are equally divided into 850 blocks, and then they are given to the model 3C-LRF-ELM to generate the labels. The final label of each image is defined by the optimal threshold \(\alpha\). The 3C-LRF-ELM can be single layer network or multi-layer network. In this paper, considering the computational complexity, we choose the single layer 3C-LRF-ELM and two layers 3C-LRF-ELM structure to analyze the influence of the number of layers on the experimental results. The experimental results show that the single layer 3C-LRF-ELM structure is better than two layers 3C-LRF-ELM. Compared to the Discriminative Feature-oriented Dictionary Learning, the single layer 3C-LRF-ELM has a better classification performance.
Similar content being viewed by others
References
Ge Q, Shao T, Yang Q, Shen X, Wen C (2016) Multisensor nonlinear fusion methods based on adaptive ensemble fifth-degree iterated cubature information filter for biomechatronics. IEEE Trans Syst Man Cybern 46(7):912–925
Magoulas GD, Prentza A (2001) Machine learning in medical applications[M]//Machine learning and its applications. Springer, Berlin, Heidelberg, pp 300–307
Lorente D, Martłnez-Martłnez F, Ruprez MJ et al (2017) A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Syst Appl 71:342–357
Drukker K, Huynh BQ, Giger ML et al (2017) Deep learning and three-compartment breast imaging in breast cancer diagnosis. Medical imaging 2017: computer-aided diagnosis. Int Soc Opt Photonics 10134:101341F
Dundar MM et al (2011) Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Signal Process 58(7):1977–1984
Ehehalt S, Wiegand S, Kerner A et al (2017) Diabetes screening in overweight and obese children and adolescents: choosing the right test. Eur J Pediatr 176(1):89–97
Takahashi N, Kinoshitaa T, Ohmuraa T et al (2017) Automated method to compute Evans index for diagnosis of idiopathic normal pressure hydrocephalus on brain CT images. Soc Photo-Optical Instrum Eng 10134:101342C
Huang H, Shen L, Ford J, Gao L, Pearlman J (2005) Early lung cancer detection based on registered perfusion MRI. J Oncol 15:1080–1084
Depeursinge A, Racoceanu D, Iavindrasana J, Cohen G, Platon A, Poletti PA, Muller H (2011) Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography. Artif Intell Med 50:13–21
Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M (2011) Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans Biomed Eng 58(12):3418–3428
Gurcan MN, Madabhushi A, Rajpoot N (2010) Pattern recognition in histopathological images: An ICPR 2010 contest[M] // recognizing patterns in signals, speech, images and videos. Springer, Berlin, Heidelberg, pp 226–234
Vannagell JR, Donaldson ES, Wood EG, Parker JC (1978) The significance of vascular invasion and lymphocytic infiltration in invasive cervical cancer. Cancer 41(1):228–234
Gurcan M et al (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064
Unser M, Aldroubi A, Laine A (2003) Guest editorial: Wavelets in medical imaging. IEEE Trans Med Imag 22(3):285–288
Ozdemir E, Gunduz-Demir C (2013) A hybrid classification model for digital pathology using structural and statistical pattern recognition. IEEE Trans Med Imag 32(2):474–483
Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. IEEE Int Symp Biomed Imaging 29:496–499
Vu TH, Mousavi HS, Monga V et al (2016) Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Trans Med Imaging 35(3):738–751
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
Huang GB, Bai Z, Kasun LLC et al (2015) Local receptive fields based extreme learning machine. IEEE Comput Intell Mag 10(2):18–29
Lv Q, Niu X, Dou Y et al (2016) Classification of hyperspectral remote sensing image using hierarchical local-receptive-field-based extreme learning machine. IEEE Geosci Remote Sens Lett 13(3):434–438
Cao W, Wang X, Ming Z et al (2017) A review on neural networks with random weights. Neurocomputing 275:278–287
Liu H, Qin J, Sun F, Guo D (2017) Extreme kernel sparse learning for tactile object recognition. IEEE Trans Cybern 47(12):4509–4520
Fu A, Wang X (2014) A study on residence error of training an extreme learning machine and its application to evolutionary algorithms. Neurocomputing 146(1):75–82
Zhang L, Zhang D (2015) Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Trans Inst Meas 64(7):1790–1801
Zhang L, Yang J, Zhang D (2017) Domain class consistency based transfer learning for image classification across domains. Inf Sci 418:242–257
Zhang L, Zhang D (2016) Evolutionary cost-sensitive extreme learning machine. IEEE Trans Neural Netw Learn Syst 28(12):3045–3060
Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine based domain adaptation. IEEE Trans Image Process 25(10):4959–4973
Yang Y, Wu QM (2016) Multilayer extreme learning machine with subnetwork nodes for representation learning. IEEE Trans Cybern 46(11):2570–2583
Zhu H, Tsang E, Wang X, Ashfaq RAR (2017) Monotonic classification extreme learning machine. Neurocomputing 225:205–213
Zhang L, He Z, Liu Y (2017) Deep object recognition across domains based on adaptive extreme learning machine. Neurocomputing 239:194–203
Ge Q, Shao T, Duan Z, Wen C (2016) Performance analysis of the kalman filter with mismatched measurement noise covariance. IEEE Trans Autom Control 61(12):4014–4019
Ge Q, Shao T, Chen S, Wen C (2017) Carrier tracking estimation analysis by using the extended strong tracking filtering. IEEE Trans Industr Electron 64(2):1415–1424
Liu H, Wu Y, Sun F, Fang B, Guo D (2018) Weakly paired multimodal fusion for object recognition. IEEE Trans Autom Sci Eng 15(2):784–795
Liu H, Sun F, Fang B, Lu S (2018) Multi-modal measurements fusion for surface material categorization. IEEE Trans Instrum Meas 67(2):246–256
Liu H, Yu Y, Sun F, Gu J (2017) Visual-tactile fusion for object recognition. IEEE Trans Autom Sci Eng 14(2):996–1008
Liu H, Guo D, Sun F (2016) Object recognition using tactile measurements: Kernel sparse coding methods. IEEE Trans Instrum Meas 65(3):656–665
Chacko BP, Krishnan VRV, Raju G et al (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybern 3(2):149–161
Fu A, Dong C, Wang L (2015) An experimental study on stability and generalization of extreme learning machines. Int J Mach Learn Cybern 6(1):129–135
Balasundaram S, Gupta D (2016) On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int J Mach Learn Cybern 7(5):707–728
Dong LT, Mintram R (2010) Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern 1(1–4):75–87
Liu H, Sun F, Guo D, Fang B (2017) Structured output-associated dictionary learning for haptic understanding. IEEE Trans Syst Man Cybern Syst 47(7):1564–1574
Redsell SA, Weng S, Swift JA et al (2016) Validation, optimal threshold determination, and clinical utility of the infant risk of overweight checklist for early prevention of child overweight. Child Obes 12(3):202–209
Alexe G, Dalgin GS, Scanfeld D, Tamayo P et al (2007) High expression of lymphocyte-associated genes in node negative her2+ breast cancers correlates with lower recurrence rates. Cancer Res 67(22):669–676
Tong B, Xu C (2016) A stone texture classification method based on local receptive field extreme learning machine. J Minnan Normal Univ (Nat.Sci.) 29(3):27–36
Swain G, Lenka SK (2012) A Better RGB Channel Based Image Steganography Technique. In: Krishna PV, Babu MR, Ariwa E (eds) Global trends in information systems and software applications. Communications in Computer and Information Science. Springer, Berlin Heidelberg
Tiwari N, Shandilya M (2010) Secure RGB image steganography from pixel indicator to triple algorithm—an incremental growth. Int J Secur Appl 4(4):53–62
Li B, Hui M, Zhu Y et al (2016) A path planner based on multivariant optimization algorithm with absorption. Int J Mach Learn Cybern 8(6):1–8
Mallick S, Kar R, Mandal D et al (2017) Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization. Int J Mach Learn Cybern 8(1):309–331
Agarwal S (2016) Critical parameter analysis of Vertical Hoeffding Tree for optimized performance using SAMOA. Int J Mach Learn Cybern 8(4):1–14
Shi Y, Gao Y, Yang Y et al (2013) Multimodal sparse representation-based classification for lung needle biopsy images. IEEE Trans Biomed Eng 60(10):2675–2685
Ge Q, Wen C, Duan S (2014) Fire localization based on range-range-range model for limited interior space. IEEE Trans Instrum Meas 63(9):2223–2237
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant U1613212 and 61673238, in part by the National High-Tech Research and Development Plan under Grant 2015AA042306, in part by the National Key R&D Program of China under Grant No.20-16YFB0100903.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Jing Fang, Xinying Xu, Huaping Liu, and Fuchun Sun declare that they no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Rights and permissions
About this article
Cite this article
Fang, J., Xu, X., Liu, H. et al. Local receptive field based extreme learning machine with three channels for histopathological image classification. Int. J. Mach. Learn. & Cyber. 10, 1437–1447 (2019). https://doi.org/10.1007/s13042-018-0825-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-018-0825-6