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Interpreting SVM for medical images using Quadtree

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Abstract

In this paper, we propose a quadtree based approach to capture the spatial information of medical images for explaining nonlinear SVM prediction. In medical image classification, interpretability becomes important to understand why the adopted model works. Explaining an SVM prediction is difficult due to implicit mapping done in kernel classification is uninformative about the position of data points in the feature space and the nature of the separating hyperplane in the original space. The proposed method finds ROIs which contain the discriminative regions behind the prediction. Localization of the discriminative region in small boxes can help in interpreting the prediction by SVM. Quadtree decomposition is applied recursively before applying SVMs on sub images and model identified ROIs are highlighted. Pictorial results of experiments on various medical image datasets prove the effectiveness of this approach. We validate the correctness of our method by applying occlusion methods.

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Notes

  1. Codes were available at https://github.com/cauchyturing/kaggle diabetic RAM

  2. http://peipa.essex.ac.uk/info/mias.html

  3. https://idrid.grand-challenge.org/

  4. https://github.com/ieee8023/covid-chestxray-dataset

  5. https://github.com/UCSD-AI4H/COVID-CT

  6. https://www.kaggle.com/tourist55/Alzheimers-dataset-4-class-of-images

References

  1. Abdullah N, Ngah UK, Aziz SA (2011) Image classification of brain mri using support vector machine. In: 2011 IEEE International conference on imaging systems and techniques. IEEE, pp 242–247

  2. Achmad A, Achmad AD, et al. (2019) Backpropagation performance against support vector machine in detecting tuberculosis based on lung x-ray image. In: First international conference on materials engineering and management-engineering section (ICMEMe 2018). Atlantis press

  3. Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, Russell S, Abràmoff M, Soliz P (2010) Multiscale am-fm methods for diabetic retinopathy lesion detection. IEEE Trans Med Imag 29(2):502–512

    Article  Google Scholar 

  4. Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 45:161–171

    Article  Google Scholar 

  5. Barakat N, Bradley AP (2010) Rule extraction from support vector machines: a review. Neurocomputing 74(1):178–190

    Article  Google Scholar 

  6. Barakat N, Diederich J (2004) Learning-based rule-extraction from support vector machines: performance on benchmark data sets. In: 3rd conference on neuro-computing and evolving intelligence (NCEI’04)

  7. Barakat NH, Bradley AP (2007) Rule extraction from support vector machines: a sequential covering approach. IEEE Trans Knowl Data Eng 19(6):729–741

    Article  Google Scholar 

  8. Bauer S, Nolte LP, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 354–361

  9. Caragea D, Cook D, Honavar VG (2001) Gaining insights into support vector machine pattern classifiers using projection-based tour methods. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 251–256

  10. Chatchinarat A, Wong KW, Fung CC (2017) Rule extraction from electroencephalogram signals using support vector machine. In: Knowledge and smart technology (KST), 2017 9th international conference on, pp. 106–110. IEEE

  11. Chung KL, Tseng SY (2001) New progressive image transmission based on quadtree and shading approach with resolution control. Pattern Recogn Lett 22(14):1545–1555

    Article  Google Scholar 

  12. Cohen JP, Morrison P, Dao L (2020) Covid-19 image data collection. arXiv:2003.11597. https://github.com/ieee8023/covid-chestxray-dataset

  13. Dhahbi S, Barhoumi W, Zagrouba E (2015) Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput Biol Med 64:79–90

    Article  Google Scholar 

  14. Doshi-Velez F, Kim B (2017)

  15. Galil Z (1980) Finding the vertex connectivity of graphs. SIAM J Comput 9(1):197–199

    Article  MathSciNet  Google Scholar 

  16. Hassanien AE, Mahdy LN, Ezzat KA, Elmousalami HH, Ella HA (2020) Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine medRxiv

  17. He J, Hu HJ, Harrison R, Tai PC, Pan Y (2006) Rule generation for protein secondary structure prediction with support vector machines and decision tree. IEEE Trans Nanobiosc 5(1):46–53

    Article  Google Scholar 

  18. Hundekar S, Chakrasali S (2019) Detection and classification of breast cancer using support vector machine and artificial neural network using contourlet transform ICTACT. J Image Video Process 9(3)

  19. Jakulin A, Možina M, Demšar J, Bratko I, Zupan B (2005) Nomograms for visualizing support vector machines. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 108–117

  20. Jakulin A, Možina M, Demšar J, Bratko I, Zupan B (2005) Nomograms for visualizing support vector machines. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 108–117

  21. Langote VB, Chaudhari D (2012) Segmentation techniques for image analysis. Int J Adv Eng Res Stud 1(2):252–255

    Google Scholar 

  22. Liu W, Ma X, Zhou Y, Tao D, Cheng J (2018) P-laplacian regularization for scene recognition. IEEE Trans Cybern PP:1–14. https://doi.org/10.1109/TCYB.2018.2833843

    Article  Google Scholar 

  23. Ma X, Liu W, Li S, Tao D, Zhou Y (2018) Hypergraph p-laplacian regularization for remotely sensed image recognition. IEEE Trans Geosci Remote Sens PP:1–11. https://doi.org/10.1109/TGRS.2018.2867570

    Google Scholar 

  24. Nandpuru HB, Salankar S, Bora V (2014) Mri brain cancer classification using support vector machine. In: 2014 IEEE Students’ conference on electrical, electronics and computer science. IEEE, pp 1–6

  25. Nguyen DH, Le MT (2014) Improving the interpretability of support vector machines-based fuzzy rules. arXiv:1408.5246

  26. Núñez H, Angulo C, Català A (2002) Rule extraction from support vector machines. In: Esann , pp 107–112

  27. de Oliveira FSS, de Carvalho Filho AO, Silva AC, de Paiva AC, Gattass M (2015) Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and svm. Comput Biol Med 57:42–53

    Article  Google Scholar 

  28. Othman MFB, Abdullah NB, Kamal NFB (2011) Mri brain classification using support vector machine. In: 2011 Fourth international conference on modeling, simulation and applied optimization. IEEE , pp 1–4

  29. Posso M, Puig T, Carles M, Rué M, Canelo-Aybar C, Bonfill X (2017) Effectiveness and cost-effectiveness of double reading in digital mammography screening: a systematic review and meta-analysis. Eur J Radiol 96:40–49

    Article  Google Scholar 

  30. Poulet F (2004) Svm and graphical algorithms: a cooperative approach. In: Data mining, 2004. ICDM’04. Fourth IEEE international conference on. IEEE, pp 499–502

  31. Rastgarpour M, Shanbehzadeh J (2011) Application of ai techniques in medical image segmentation and novel categorization of available methods and tool. In: Proceedings of the international multiconference of engineers and computer scientists 2011 Vol I, IMECS 2011, March 16-18, 2011, Hong Kong. Citeseer

  32. Salas-Gonzalez D, Górriz JM, Ramírez J, López M, Álvarez I, Segovia F, Chaves R, Puntonet C (2010) Computer-aided diagnosis of alzheimer’s disease using support vector machines and classification trees. Phys Med Biol 55(10):2807

    Article  Google Scholar 

  33. Sidibé D, Sadek I, Mériaudeau F (2015) Discrimination of retinal images containing bright lesions using sparse coded features and svm. Comput Biol Med 62:175–184

    Article  Google Scholar 

  34. Smilkov D, Thorat N, Kim B, Viégas F, Wattenberg M (2017)

  35. Solanke A, Manjunath R, Jadhav D (2019) Classification of masses as malignant or benign using support vector machine. Lung 20(176):61–007

    Google Scholar 

  36. Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage learning

  37. Sun Z, Qiao Y, Lelieveldt BP, Staring M, Initiative ADN, et al. (2018) Integrating spatial-anatomical regularization and structure sparsity into svm: Improving interpretation of alzheimer’s disease classification. NeuroImage 178:445–460

    Article  Google Scholar 

  38. Tao D, Jin L, Liu W, Li X (2013) Hessian regularized support vector machines for mobile image annotation on the cloud. IEEE Trans Multimed 15(4):833–844

    Article  Google Scholar 

  39. Tseng SY, Yang ZY, Huang WH, Liu CY, Lin YH (2009) Object feature extraction for image retrieval based on quadtree segmented blocks. In: 2009 World congress on computer science and information engineering. IEEE, pp 401–405

  40. Wang X, Wu S, Wang X, Li Q (2006) Svmv–a novel algorithm for the visualization of svm classification results. In: International symposium on neural networks. Springer, pp 968–973

  41. Wang Z, Yang J (2018) Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. In: Workshops at the thirty-second AAAI conference on artificial intelligence

  42. Sampaio WB, Diniz EM, Silva AC, de Paiva AC, Gattass M (2011) Detection of masses in mammogram images using cnn, geostatistic functions and svm. Comput Biol Med 41:653–664

    Article  Google Scholar 

  43. Yahyaoui A, Yumuṡak N (2018) Decision support system based on the support vector machines and the adaptive support vector machines algorithm for solving chest disease diagnosis problems

  44. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818–833

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Authors and Affiliations

  1. Department of IT, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Allahabad, UP, India

    Prashant Shukla, Abhishek Verma,  Abhishek, Shekhar Verma & Manish Kumar

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  1. Prashant Shukla
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Shukla, P., Verma, A., Abhishek et al. Interpreting SVM for medical images using Quadtree. Multimed Tools Appl 79, 29353–29373 (2020). https://doi.org/10.1007/s11042-020-09431-2

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