Automatic Blood-Cell Classification via Convolutional Neural Networks and Transfer Learning

Authors

Keywords:

Cancer Cell Detection, Convolutional Neural Networks, Residual Neural Network, Transfer Learning

Abstract

The evaluation and diagnosis of cancer related diseases can be complex and lengthy. This is exacerbated due to manual analyses based on techniques that may take copious amount of time. In the last decade, different tools have been created to detect, analyze and classify different types of cancer in humans. However, there is still a lack of tools or models to automate the analysis of human cells to determine the presence of cancer. Such a model has the potential to improve early detection and prevention of said diseases, leading to more timely medical diagnoses. In this research, we present our current effort on the development of a Deep Learning Model capable of identifying blood cell anomalies. Our results show an accuracy that meets or exceeds the current state of the art, particularly achieving lower false negative rate in comparison to previous efforts reported.

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Author Biographies

Luis Claudio Soto-Ayala, Tecnologico de Monterrey

Luis Claudio Soto-Ayala received the B.Sc. degree in Computer Science from the Tecnol´ogico de Monterrey, Quer´etaro, Mexico, in 2020. His degree thesis focused on the classification of blood cells’ anomalies using Deep Learning, in particular Convolutional Neural Networks. Currently, he is working as an engineer and software developer in the financial group Santander, Mexico, Quer´etaro, in the CSA department, where he develops applications and web solutions for the optimization and automation of critical tasks, as well as the validation of their correct operation in production. claudio.soto.ayala@gmail.com

Jose Antonio Cantoral-Ceballos, Tecnologico de Monterrey

Jose A. Cantoral-Ceballos (Ph.D., M.Sc., MIEEE) received the B.Sc. (Excellence Hons.) degree in electronic and communications engineering from the Tecnologico de Monterrey, Queretaro, Mexico, in 2005, and the M.Sc. (Distinction) and Ph.D. degrees from the University of Manchester, Manchester, U.K., in 2009 and 2013, respectively, with a focus on algorithms for tomography imaging in embedded systems. During 2013-2014 he was a Post-Doctoral Research Associate at the University of Manchester where he worked on the design and development of a tomography data acquisition system, as well as imaging algorithms from limited views. From 2015 to 2020 he was at the Advanced Technology Center (CIATEQ A.C.), Mexico, where his research efforts focused on the study of novel Deep Learning methods to solve Digital Signal Processing and Energy problems. Currently, he is a Researcher Professor at the Tecnologico de Monterrey, Queretaro, in the department of Computer Science, where he continues his research on Deep Learning solutions to different problems, in particular to the study of neurological signals. Dr. Cantoral-Ceballos was the recipient of the National Instruments Prize for his M.Sc. dissertation in 2009 and the Best Technical Presentation Award at the Seventh World Congress of Industrial Process Tomography (WCIPT), Krakow, in 2013. joseantonio.cantoral@tec.mx

References

L. Zhang, H. Gao, J. Zhang and B. Badami, "Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer", Open Medicine, vol. 15, no. 1, pp. 27-37, 2020. Available: 10.1515/med-2020-0006.

T. Zhang et al., "Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images", Biomedical Signal Processing and Control, vol. 55, p. 101566, 2020. Available: 10.1016/j.bspc.2019.101566.

S. Yadav and S. Jadhav, "Deep convolutional neural network based medical image classification for disease diagnosis", Journal of Big Data, vol. 6, no.1, 2019. Available: 10.1186/s40537-019-0276-2

S. Tabibu, P. Vinod and C. Jawahar, "Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning", Scientific Reports, vol. 9, no. 1, 2019. Available: 10.1038/s41598-019-46718-3.

"Lunit Inc.", Lunit.io, 2020. [Online]. Available: https://www.lunit.io/en/. [Accessed: 27- Mar- 2020].

A. Suarez Leon and J. Nunez Alvarez, "1D Convolutional Neural Network for Detecting Ventricular Heartbeats", IEEE Latin America Transactions, vol. 17, no. 12, pp. 1970-1977, 2019. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/2850/351. [Accessed 5 September 2020].

L. Evangelista and E. Guedes, "Ensembles of Convolutional Neural Networks on Computer-Aided Pulmonary Tuberculosis Detection", IEEE Latin America Transactions, vol. 17, no. 12, pp. 1954-1963, 2019. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/2813/349. [Accessed 5 September 2020].

de Oliveira Lima, L. Silva de Araujo Filho, F. Santos da Silva and C. Serodio Figueiredo, "Pigmented Dermatological Lesions Classification Using Convolutional Neural Networks Ensemble Mediated by Multilayer Perceptron Network", IEEE Latin America Transactions, vol. 17, no. 11, pp. 1902-1908, 2019. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/1948/341. [Accessed 5 September 2020].

Javier Alvarez-Valle, Aditya Nori, Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare , et al. "Project InnerEye - Medical Imaging AI to Empower Clinicians - Microsoft Research", Microsoft Research, 2020. [Online]. Available: https://www.microsoft.com/en-us/research/project/medical-image-analysis/. [Accessed: 27- Mar- 2020].

R. Pedro da Silva Neto and A. Oseas de Carvalho Filho, "Automatic classification of breast lesions usingTransfer Learning", IEEE Latin America Transactions, vol. 17, no. 12, pp. 1964-1969, 2019. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/2837/350. [Accessed 5 September 2020].

National Cancer Institute, "What Is Cancer?", National Cancer Institute, 2020. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer. [Accessed: 18- Feb- 2020].

American Cancer Society, "¿Qué es el cáncer?", Cancer.org, 2020. [Online]. Available: https://www.cancer.org/es/cancer/aspectos-basicos-sobre-el-cancer/que-es-el-cancer.html. [Accessed: 18- Feb- 2020].

Biblioteca Nacional de Medicina, "Leucemia: MedlinePlus enciclopedia médica", Medlineplus.gov, 2020. [Online]. Available: https://medlineplus.gov/spanish/ency/article/001299.htm. [Accessed: 18- Feb- 2020].

M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger and H. Greenspan, "GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification", Neurocomputing, vol. 321, pp. 321-331, 2018. Available: 10.1016/j.neucom.2018.09.013.

Y. Zhou et al., "A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification", IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 1935-1942, 2018. Available: 10.1109/tbme.2018.2844188.

Bernal et al., "Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review", Artificial Intelligence in Medicine, vol. 95, pp. 64-81, 2019. Available: 10.1016/j.artmed.2018.08.008 [Accessed 19 May 2020].

D. Zhang et al., "Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis", Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp. 237-244, 2018. Available: 10.1007/978-3-030-00934-2_27 [Accessed 19 May 2020].

L. Zhang, F. Yin and J. Cai, "A Multi-Source Adaptive MR Image Fusion Technique for MR-Guided Radiation Therapy", International Journal of Radiation Oncology*Biology*Physics, vol. 102, no. 3, p. e552, 2018. Available: 10.1016/j.ijrobp.2018.07.1537.

S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software, vol. 95, pp. 51-67, 2016. Available: 10.1016/j.advengsoft.2016.01.008 [Accessed 19 May 2020].

Kermany D, Goldbaum M. Labeled optical coherence tomography (oct) and chest X-ray images for classifcation. In: Mendeley data. 2018. p. 2.

Y. Cao, T. A. Geddes, J. Y. H. Yang, and P. Yang, “Ensemble deep learning in bioinformatics,” Nature Machine Intelligence, vol. 2, no. 9, Art. no. 9, Sep. 2020, doi: 10.1038/s42256-020-0217-y.

M. Lopez-Martin, A. Nevado, and B. Carro, “Detection of early stages of Alzheimer’s disease based on MEG activity with a randomized convolutional neural network,” Artificial Intelligence in Medicine, vol. 107, p. 101924, Jul. 2020, doi: 10.1016/j.artmed.2020.101924.

S. H. Kassani, P. H. Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Classification of histopathological biopsy images using ensemble of deep learning networks,” in Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, USA, Nov. 2019, pp. 92–99, Accessed: Jan. 07, 2021. [Online].

C. Matek, S. Schwarz, K. Spiekermann and C. Marr, "Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks", Nature Machine Intelligence, vol. 1, no. 11, pp. 538-544, 2019. Available: 10.1038/s42256-019-0101-9.

De Fauw et al., "Clinically applicable deep learning for diagnosis and referral in retinal disease", Nature Medicine, vol. 24, no. 9, pp. 1342-1350, 2018. Available: 10.1038/s41591-018-0107-6.

Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. Available: 10.1109/5.726791.

V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines.,” in ICML, 2010, pp. 807–814.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575 [cs], Jan. 2015, Accessed: Jan. 07, 2021. [Online]. Available: http://arxiv.org/abs/1409.0575.

Matek, C., Schwarz, S., Marr, C., & Spiekermann, K. (2019). A Single-cell Morphological Dataset of Leukocytes from AML Patients and Non-malignant Controls [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.36f5o9ld

K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps,” arXiv:1312.6034 [cs], Apr. 2014, Accessed: Jan. 07, 2021. [Online]. Available: http://arxiv.org/abs/1312.6034.

G. J. Vizcaíno-Salazar, "Importancia del cálculo de la sensibilidad, la especificidad y otros parámetros estadísticos en el uso de las pruebas de diagnóstico clínico y de laboratorio", Docs.bvsalud.org, 2020. [Online]. Available: http://docs.bvsalud.org/biblioref/2018/05/883697/importancia-calculo-sensibilidad-y-especifidad.pdf. [Accessed: 15- Apr- 2020].

M. Sánchez Nava, A. Olivares Montano, N. Contreras Carreto and M. Díaz Suárez, "Certeza diagnóstica de la colposcopia, citología e histología de las lesiones intraepiteliales del cérvix", Rev Invest Med Sur Mex, vol. 20, no. 2, pp. Paginas: 95-99, 2020. Available: https://www.medigraphic.com/pdfs/medsur/ms-2013/ms132b.pdf. [Accessed 19 May 2020].

E. Horvath, M. Galleguillos and V. Schonstedt, "¿Existen cánceres no detectables en la mamografia?", Revista Chilena de Radiología, vol. 13, no. 2, pp. 84-89, 2020. Available: https://scielo.conicyt.cl/pdf/rchradiol/v13n2/art07old.pdf. [Accessed 15 April 2020].

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Published

2021-05-26

How to Cite

Soto-Ayala, L. C., & Cantoral-Ceballos, J. A. (2021). Automatic Blood-Cell Classification via Convolutional Neural Networks and Transfer Learning. IEEE Latin America Transactions, 19(12), 2028–2036. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5051

Issue

Vol. 19 No. 12 (2021): Ordinary Issue

Section

Articles