Advertisement

Deep learning approach to detect malaria from microscopic images

  • Vijayalakshmi A
  • Rajesh Kanna B
Article
  • 8 Downloads

Abstract

Malaria is an infectious disease which is caused by plasmodium parasite. Several image processing and machine learning based techniques have been employed to diagnose malaria, using its spatial features extracted from microscopic images. In this work, a novel deep neural network model is introduced for identifying infected falciparum malaria parasite using transfer learning approach. This proposed transfer learning approach can be achieved by unifying existing Visual Geometry Group (VGG) network and Support Vector Machine (SVM). Implementation of this unification is carried out by using “Train top layers and freeze out rest of the layers” strategy. Here, the pre-trained VGG facilitates the role of expert learning model and SVM as domain specific learning model. Initial ‘k’ layers of pre-trained VGG are retained and (n-k) layers are replaced with SVM. To evaluate the proposed VGG-SVM model, a malaria digital corpus has been generated by acquiring blood smear images of infected and non-infected malaria patients and compared with state-of-the-art Convolutional Neural Network (CNN) models. Malaria digital corpus images were used to analyse the performance of VGG19-SVM, resulting in classification accuracy of 93.1% in identification of infected falciparum malaria. Unification of VGG19-SVM shows superiority over the existing CNN models in all performance indicators such as accuracy, sensitivity, specificity, precision and F-Score. The obtained result shows the potential of transfer learning in the field of medical image analysis, especially malaria diagnosis.

Keywords

Malaria Convolutional neural network Transfer learning VGG16 VGG19 Support vector machine Deep learning 

Notes

Acknowledgements

We would like to acknowledge Tagore Medical College & Hospital, Chennai for providing malaria smear specimen during the course of this research. And also we would like to thank FIMM for providing Mamic web microscopic database for open access.

References

  1. 1.
    Abbas N, Mohamad D (2013) Microscopic RGB color images enhancement for blood cells segmentation in YCBCR color space for k-means clustering. J Theor Appl Inf Technol 55(1):117–125Google Scholar
  2. 2.
    Abu NS, Ashidi NMI, Chia LL, Mohamed Z, Kalthum UN, Zuhairi KZ (2008) Classification of malaria parasite species based on thin blood smears using multilayer perceptron network. Int J Comput Int Manag 16(1):46–52Google Scholar
  3. 3.
    Ahn E, Kumar A, Kim J, Li C, Feng D, Fulham M (2016) X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid, IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 855–858Google Scholar
  4. 4.
    Anggraini D, Nugroho AS, Pratama C, Rozi IE, Pragesjvara V, Gunawan M (2011) Automated status identification of microscopic images obtained from malaria thin blood smears using bayes decision: a study case in Plasmodium falciparum. International Conference on Advanced Computer Science and Information Systems, Jakarta, pp 347–352Google Scholar
  5. 5.
    Arco JE, Gorriz JM, Ramírez J, Alvarez I, Puntonet CG (2015) Digital image analysis for automatic enumeration of malaria parasites using morphological operations. Expert Syst Appl 42:3041–3047CrossRefGoogle Scholar
  6. 6.
    Boray Tek F, Dempster AG, Kale İ (2010) Parasite detection and identification for automated thin blood film malaria diagnosis. Comput Vis Image Underst 114(1):21–32CrossRefGoogle Scholar
  7. 7.
    Chavan SN, Sutkar AM (2014) Malaria disease identification and analysis using image processing. Int J Latest Trends Eng Technol 3:218–223Google Scholar
  8. 8.
    Chayadevi M, Raju G (2014) Usage of art for automatic malaria parasite identification based on fractal features. Int J Video Image Proc Netw Sec 14(4):7–15Google Scholar
  9. 9.
    Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S (2017) Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inform 21(1):76–84CrossRefGoogle Scholar
  10. 10.
    Damahe LB, Krishna R, Janwe N (2011) Segmentation based approach to detect parasites and RBCs in blood cell images. Int J Comput Sci Appl 4(2):71–81Google Scholar
  11. 11.
    Das DK, Maiti AK, Chakraborty C (2015) Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J Microsc 257(3):238–252CrossRefGoogle Scholar
  12. 12.
    Díaz G, González FA, Romero E (2009) A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. J Biomed Inform 42(2):296–307CrossRefGoogle Scholar
  13. 13.
    Dong Y et al (2017) Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, pp 101–104Google Scholar
  14. 14.
    Hung J, Carpenter A (2017) Applying Faster R-CNN for Object Detection on Malaria Images, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 808–813Google Scholar
  15. 15.
    Jackson Samuel RD, Rajesh Kanna B (2018) Tuberculosis Detection system using Deep neural networks, Neural Computing and Applications, Springer, Article in PressGoogle Scholar
  16. 16.
    Jackson Samuel RD, Rajesh Kanna B (2018) Cybernetic microbial detection system using transformation learning, Multimedia Tools and Applications, Springer, Article in PressGoogle Scholar
  17. 17.
    Kermany DS et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131CrossRefGoogle Scholar
  18. 18.
    Khan NA, Pervaz H, Latif AK, Musharraf A, Saniya (2014) Unsupervised identification of malaria parasites using computer vision, 11th international joint conference on computer science and software engineering (JCSSE). Chon Buri:263–267Google Scholar
  19. 19.
    Khot ST, Prasad RK (2014) Optimal computer based analysis for detecting malarial parasites, proceedings of the 3rd international conference on Frontiers of intelligent computing: theory and applications (FICTA). Adv Intel Syst Comput, Springer, Cham 327:69–80Google Scholar
  20. 20.
    Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25:1097–1105Google Scholar
  21. 21.
    Kumarasamy SK, Ong SH, Tan KSW (2011) Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection. Mach Vis Appl 22(3):461–469Google Scholar
  22. 22.
    Le M-T, Bretschneider TR, Kuss C, Preiser PR (2008) A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biol 9(15):1–12Google Scholar
  23. 23.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2323CrossRefGoogle Scholar
  24. 24.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data, In: Artificial Intelligence (IJCAI), 1–8Google Scholar
  25. 25.
    Ma C, Harrison P, Wang L, Coppel RL (2010) Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears. Malaria J 9(348)Google Scholar
  26. 26.
    Makkapati VV, Rao RM (2011) Ontology-based malaria parasite stage and species identification from peripheral blood smear images. Ann Int Conf IEEE Eng Med Biol Soc:6138–6141Google Scholar
  27. 27.
    Malihi L,Ansari-Asl K,Behbahani A (2013) Malaria parasite detection in giemsa-stained blood cell images, 8th Iranian Conference on Machine Vision and Image Processing (MVIP), Zanjan. 360–365Google Scholar
  28. 28.
    Mandal S, Kumar A, Chatterjee J, Manjunatha M, Ray AK (2010) Segmentation of blood smear images using normalized cuts for detection of malarial parasites. Annual IEEE India Conference (INDICON), Kolkata, pp 1–4Google Scholar
  29. 29.
    May Z, Mohd Aziz SSA, Salamat R (2013) Automated quantification and classification of malaria parasites in thin blood smears. IEEE International Conference on Signal and Image Processing Applications, Melaka, pp 369–373Google Scholar
  30. 30.
    Memeu, Daniel Mathethia (2014) A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites In stained thin blood smear images, University of NairobiGoogle Scholar
  31. 31.
    Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp 1717–1724Google Scholar
  32. 32.
    Pallavi T (2013) Suradkar, detection of malarial parasite in blood using image processing. Int J Eng Inn Technol (IJEIT) 2(10):124–126Google Scholar
  33. 33.
    Pan WD, Dong Y, Wu D (2017) Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks, In Machine Learning-Advanced Techniques and Emerging Applications. Intech OpenGoogle Scholar
  34. 34.
    Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G (2018) Image analysis and machine learning for detecting malaria. Trans Res: J Lab Clin Med 194:36–55CrossRefGoogle Scholar
  35. 35.
    Prasad K, Winter J, Bhat UM, Acharya RV, Prabhu GK (2012) Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images. J Digit Imaging 25(4):542–549CrossRefGoogle Scholar
  36. 36.
    Rajaraman S et al. (2018) Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. Peer JGoogle Scholar
  37. 37.
    Savkare SS, Narote SP (2015) Automated system for malaria parasite identification. International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, pp 1–4Google Scholar
  38. 38.
    Schapire RE (2013) Explaining ada boost. Empirical inference, Springer, Berlin, pp 37–52zbMATHGoogle Scholar
  39. 39.
    Simonyan, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition, Computer Vision and Pattern Recognition, arXiv:1409.1556Google Scholar
  40. 40.
    Soni J, Mishra N, Kamargaonkar (2011) Automatic difference between RBC and malaria parasites based on morphology with first order features using image processing. Int J Adv Eng Technol 1:290–297Google Scholar
  41. 41.
    Špringl V (2009) Automatic malaria diagnosis through microscopy imaging. Higher Diploma, Faculty of electrical engineering, Czech Technical University in Prague, PragueGoogle Scholar
  42. 42.
    Szegedy C et al (2015) Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp 1–9Google Scholar
  43. 43.
    Wang SH, Lv YD, Sui Y, Liu S, Wang SJ, Zhang YD (2018) Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J Med Syst 42(1):2CrossRefGoogle Scholar
  44. 44.
    WebMicroscope “Institute for molecular medicine Finland and FIMM”. http://fimm.webmicroscope.net/Research/Momic/mamic. Accessed 1 June 2018
  45. 45.
    World Health Organization World Malaria Report – 2017. http://www.who.int/malaria/publications/world-malaria-report-2017/en. Accessed Jan 2018
  46. 46.
    Yann LC, Bengio Y, Hinto G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  47. 47.
    Yunda L, Alarcón A, Millán J (2012) Automated image analysis method for p-vivax malaria parasite detection in thick film blood images. Sistemas y Telemática 10(20):9–25CrossRefGoogle Scholar
  48. 48.
    Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional neural networks, European conference on computer vision, 818–833Google Scholar
  49. 49.
    Zhang YD, Jiang Y, Zhu W, Lu S, Zhao G (2017) Exploring a smart pathological brain detection method on pseudo Zernike moment. Multimed Tools ApplGoogle Scholar
  50. 50.
    Zhang YD, Pan C, Chen X, Wang F (2018) Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J Comput Sci 27:57–68CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Vellore Institute of TechnologySchool of Computing Science and EngineeringChennaiIndia

Personalised recommendations