A Leukemia Diagnosis System Using Pre-trained CNNs and a Committee of Classifiers

• Luis H. S. Vogado UFPI
• Rodrigo M. S. Veras UFPI
• Alan R. Andrade UFPI
• Luís G. T. Santos UFPI
• Kelson R. T. Aires UFPI
• Vinicius P. Machado UFPI

DOI: https://doi.org/10.5753/sbcas.2017.3719

Abstract

Leukemia is among the diseases that afflict more young people and adults, thus causing an early death. To assist the experts in diagnosing this disease, there are computer-aid systems. These systems prevent diagnosis from being affected by variables such as experience and the tiredness of the specialist, collaborating with a prescription medication and avoiding inappropriate treatment. In this work, a new methodology for the creation of a system of diagnosis of leukemia with the use of CNN’s, PCA and an ensemble of classifiers. The ALL-IDB1 database was used in conducting the experiments and obtained 98.14% accuracy, overlapping the results presented in the literature.

References

Agaian, S., Madhukar, M., and Chronopoulos, A. T. (2016). A new acute leukaemiaautomated classification system. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pages 1–12.

Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. CoRR, abs/1508.00092.

Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. In British Machine Vision Conference.

Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., and Liu, J. (2015). Glaucoma detection based on deep convolutional neural network. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), volume 37, pages 715–718.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22Nd ACM International Conference on Multimedia, MM ’14, pages 675–678, New York, NY, USA. ACM.

Jr., M. P. P. (2011). Combining classifiers: From the creation of ensembles to the decision fusion. In SIBGRAPI Tutorials, pages 1–10. IEEE Computer Society.

Kawahara, J. and Hamarneh, G. (2016). Multi-resolution-Tract CNN with Hybrid Pre-trained and Skin-Lesion Trained Layers. Springer International Publishing, Cham.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc.

Kumar, A., Kim, J., Lyndon, D., Fulham, M., and Feng, D. (2016). An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE Journal of Biomedical and Health Informatics, PP:1–9.

Labati, R. D., Piuri, V., and Scotti, F. (2011). All-idb: The acute lymphoblastic leukemia image database for image processing. Image Processing (ICIP), 2011 18th IEEE International Conference on, pages 2045–2048.

Madhukar, M., Agaian, S., and Chronopoulos, A. T. (2012). New decision support tool for acute lymphoblastic leukemia classification. Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 8295.

Madhukar, M., Agaian, S., and Chronopoulos, A. T. (2014). Automated screening system for acute myelogenous leukemia detection in blood microscopic images. IEEE Systems Journal, 8(3).

Mohapatra, S., Patra, D., and Satpathy, S. (2014). An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Computing and Applications, 24:1887–1904.

Patel, N. and Mishra, A. (2015). Automated leukaemia detection using microscopic images. 58:635–642.

Shin, H., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. J., and Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. CoRR, abs/1602.03409.

Vincent, I., Kwon, K.-R., Lee, S.-H., and Moon, K.-S. (2015). Acute lymphoid leukemia classification using two-step neural network classifier. Frontiers of Computer Vision (FCV), pages 1–4.

Vogado, L. H. S., de M S Veras, R., Andrade, A. R., e Silva, R. R. V., de Araujo, F. H. D., and de Medeiros, F. N. S. (2016). Unsupervised leukemia cells segmentation based on multi-space color channels. In International Symposium on Multimedia, pages 451–456. IEEE.

Wang, D., Khosla, A., Gargeya, R., Irshad, H., and Beck, A. H. (2016). Deep Learning for Identifying Metastatic Breast Cancer. ArXiv e-prints.

Zack, G. W., Rogers, W. E., and Latt, S. A. (1977). Automatic measurement of sister chromatid exchange frequency. Journal of Histochemistry & Cytochemistry, pages 741–753.