Abstract
Acute Lymphocytic or Lymphoblastic Leukemia (ALL) is a virulent form of blood cancer that affects white blood cells and the bone-marrow–spongy tissue. At the start of ALL, immature white blood cells proliferate and replace healthy cells in the bone marrow. ALL progresses quickly and can be fatal within a few months if not treated. Computer assisted diagnosis and prognosis of ALL, therefore, has the potential to save many lives but requires high accuracy classification of malignant cells which is challenging due to the visual similarity between normal and malignant cells. In this work, we employ a custom-built deep learning model for the classification of immature lymphoblasts and normal cells. Our model is an ensemble of convolutional and recurrent neural networks. It also exploits the spectral features of the cells by using discrete cosine transform in conjunction with an RNN. The proposed classifier has been validated using multiple experiments. Our approach is able to achieve substantial performance gains when compared to, conventional, stand-alone CNN- and RNN-based methods. The highest accuracy achieved by our model is 86.6%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bi, L., Kim, J., Ahn, E., Kumar, A., Fulham, M., Feng, D.: Dermoscopic image segmentation via multistage fully convolutional networks. IEEE Trans. Biomed. Eng. 64(9), 2065–2074 (2017)
Chougrad, H., Zouaki, H., Alheyane, O.: Deep convolutional neural networks for breast cancer screening. Comput. Methods Programs Biomed. 157, 19–30 (2018)
Duggal, R., Gupta, A., Gupta, R.: Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks. In: CME Series on Hemato-Oncopathology. AIIMS (2016)
Duggal, R., Gupta, A., Gupta, R., Mallick, P.: SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 435–443. Springer (2017)
Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., Ahuja, C.: Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, p. 82. ACM (2016)
Gupta, A., Gupta, R., Kumar, L., Thakkar, N., Satpathy, D.: GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images (2018)
Gupta, R., Mallick, P., Duggal, R., Gupta, A., Sharma, O.: Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma. Clin. Lymphoma Myeloma Leukemia 17(1), e99 (2017)
Hochreiter, S., urgen Schmidhuber, J., Elvezia, C.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Kant, S., Kumar, P., Gupta, A., Gupta, R., et al.: LeukoNet: DCT-based CNN architecture for the classification of normal versus leukemic blasts in B-ALL cancer (2018). arXiv:1810.07961
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017)
Li, Y., Zhu, R., Mi, L., Cao, Y., Yao, D.: Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Comput. Math. Methods Med. 2016 (2016)
Nawaz, W., Ahmed, S., Tahir, A., Khan, H.A.: Classification of breast cancer histology images using ALEXNET. In: International Conference Image Analysis and Recognition, pp. 869–876. Springer (2018)
Negm, A.S., Hassan, O.A., Kandil, A.H.: A decision support system for acute Leukaemia classification based on digital microscopic images. Alexandria Eng. J. (2017)
Nguyen, L.D., Lin, D., Lin, Z., Cao, J.: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.Y.: Convolutional-recursive deep learning for 3D object classification. In: Advances in Neural Information Processing Systems, pp. 656–664 (2012)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Wang, X., Jiang, W., Luo, Z.: Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2428–2437 (2016)
Weir, E.G., Borowitz, M.J.: Flow cytometry in the diagnosis of acute leukemia. In: Seminars in Hematology, vol. 38, pp. 124–138. Elsevier (2001)
Zhang, L., Lu, L., Nogues, I., Summers, R.M., Liu, S., Yao, J.: DeepPap: deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inform. 21(6), 1633–1643 (2017)
Zuo, H., Fan, H., Blasch, E., Ling, H.: Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24(3), 289–293 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shah, S., Nawaz, W., Jalil, B., Khan, H.A. (2019). Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_3
Download citation
DOI: https://doi.org/10.1007/978-981-15-0798-4_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0797-7
Online ISBN: 978-981-15-0798-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)