"LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia
Resumo
Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.Referências
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J. Yanas and E. Triantaphyllou, "A systematic survey of computer-aided diagnosis in medicine: Past and present developments," Expert Systems with Applications, vol. 138, p. 112821, December 2019.
X. Li, L. Liu, J. Zhou, and C. Wang, "Heterogeneity analysis and diagnosis of complex diseases based on deep learning method," Scientific Reports, vol. 8, no. 6155, pp. 1–8, 2018.
F. P. dos Santos and M. A. Ponti, "Alignment of local and global features from multiple layers of convolutional neural network for image classifi- cation," in 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2019, pp. 241–248.
L. H. S. Vogado, R. M. S. Veras, F. H. D. Araújo, R. R. V. e Silva, and K. R. T. Aires, "Leukemia diagnosis in blood slides using transfer learning in cnns and SVM for classification," Engineering Applications of Artificial Intelligence, vol. 72, pp. 415–422, 2018.
N. Patel and A. Mishra, "Automated leukaemia detection using micro- scopic images," Procedia Computer Science, vol. 58, pp. 635–642, 2015.
V. Singhal and P. Singh, Texture Features for the Detection of Acute Singapore: Springer Singapore, 2016, vol. Lymphoblastic Leukemia. 409, pp. 535–543.
T. T. P. Thanh, C. Vununu, S. Atoev, S.-H. Lee, and K.-R. Kwon, "Leukemia blood cell image classification using convolutional neural network," International Journal of Computer Theory and Engineering, vol. 10, no. 2, pp. 54–58, 2018.
S. Shafique and S. Tehsin, "Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks," Technology in Cancer Research and Treatment, vol. 17, pp. 1–7, september 2018.
A. Rehman, N. Abbas, T. Saba, S. I. ur Rahman, Z. Mehmood, and H. Kolivand, "Classification of acute lymphoblastic leukemia using deep learning," Microscopy Research and Technique, pp. 1–8, October 2018.
R. Sipes and D. Li, "Using convolutional neural networks for automated fine grained image classification of acute lymphoblastic leukemia," in 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA), July 2018, pp. 157–161.
T. Pansombut, S. Wikaisuksakul, K. Khongkraphan, and A. Phon-on, "Convolutional neural networks for recognition of lymphoblast cell images," Computational Intelligence and Neuroscience, vol. 2019, pp. 1–12, 2019.
N. Ahmed, A. Yigit, Z. Isik, and A. Alpkocak, "Identification of leukemia subtypes from microscopic images using convolutional neural network," Diagnostics, vol. 9, no. 3, pp. 1–11, 2019.
R. D. Labati, V. Piuri, and F. Scotti, "All-idb: The acute lymphoblastic leukemia image database for image processing." in 18th IEEE Interna- tional Conference on Image Processing (ICIP), 2011, pp. 2045–2048.
O. Sarrafzadeh and A. M. Dehnavi, "Nucleus and cytoplasm segmen- tation in microscopic images using k means clustering and region growing," Advanced Biomedical Research, pp. 79–87, December 2015.
M. Rollins-Raval, J. Raval, and L. Contis, "Experience with cellavision dm96 for peripheral blood differentials in a large multi-center academic hospital system," Journal of Pathology Informatics, vol. 3, no. 29, pp. 1–9, 2012.
A. T. H. U. B. Omid Sarrafzadeh, Hossein Rabbani, "Selection of the best features for leukocytes classification in blood smear microscopic images," in Proc. SPIE, vol. 9041, 2014, pp. 9041 – 9041 – 8.
O. Sarrafzadeh, H. Rabbani, A. M. Dehnavi, and A. Talebi, "Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation." in ICIP. IEEE, 2015, pp. 3339– 3343.
A. M. P. G. Vale, A. M. G. Guerreiro, A. D. D. Neto, G. B. Caval- vanti Junior, V. C. L. T. de Sá Leitão, and A. M. Martins, "Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach," Revista Brasileira de Engenharia Biomédica, vol. 30, pp. 341–354, 2014.
J. Böhm, "Pathologie-websites im world wide web," Der Pathologe, vol. 29, no. 3, pp. 231–242, 2008.
X. Zheng, Y. Wang, G. Wang, and Z. Chen, "Fast and robust segmen- tation of white blood cell images by self-supervised learning," Micron, vol. 107, pp. 55–71, 2018.
R. Duggal, A. Gupta, R. Gupta, and P. Mallick, "Sd-layer: Stain deconvolutional layer for cnns in medical microscopic imaging," in Medical Image Computing and Computer Assisted Intervention MICCAI 2017. Cham: Springer International Publishing, 2017, pp. 435–443.
S. H. Rezatofighi and H. Soltanian-Zadeh, "Automatic recognition of five types of white blood cells in peripheral blood," Computerized Medical Imaging and Graphics, vol. 35, no. 4, pp. 333 – 343, 2011.
L. Perez and J. Wang, "The effectiveness of data augmentation in image classification using deep learning," CoRR, vol. abs/1712.04621, 2017.
C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of Big Data, vol. 6, no. 1, p. 60, July 2019. [Online]. Available: https://doi.org/10.1186/ s40537-019-0197-0
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211–252, 2015.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, ser. NIPS’14, Cambridge, MA, USA, 2014, pp. 3320–3328.
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, "Convolutional neural networks for medical image analysis: Full training or fine tuning?" IEEE Transactions on Medical Imaging, vol. 35, pp. 1299–1312, 2016.
M. Izadyyazdanabadi, E. Belykh, M. Mooney, N. Martirosyan, J. Es- chbacher, P. Nakaji, M. Preul, and Y. Yang, "Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical cle images," Journal of Visual Communi- cation and Image Representation, vol. 54, pp. 10–20, 7 2018.
F. H. Araujo, R. R. Silva, F. N. Medeiros, D. D. Parkinson, A. Hexemer, C. M. Carneiro, and D. M. Ushizima, "Reverse image search for scientific data within and beyond the visible spectrum," Expert Systems with Applications, vol. 109, pp. 35–48, 2018.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," CoRR, vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2016, pp. 770–778.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in 2016 IEEE Confer- ence on Computer Vision and Pattern Recognition CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, 2016, pp. 2818–2826.
F. Chollet, "Xception: Deep learning with depthwise separable convo- lutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, 2017, pp. 1800–1807.
L. Zhang, L. Lu, I. Nogues, R. M. Summers, S. Liu, and J. Yao, "Deeppap: Deep convolutional networks for cervical cell classification," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1633–1643, 2017.
A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, and A. Navea, "Cnns for automatic glaucoma assessment using fundus im- ages: an extensive validation," BioMedical Engineering OnLine, vol. 18, no. 29, pp. 1–19, 2019.
E. Gibson, Y. Hu, H. J. Huisman, and D. C. Barratt, "Designing image segmentation studies: Statistical power, sample size and reference standard quality," Medical Image Analysis, vol. 42, pp. 44–59, 2017.
S. Kornblith, J. Shlens, and Q. V. Le, "Do better imagenet models transfer better?" in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019, pp. 2661–2671.
R. F. de Mello and M. A. Ponti, Machine Learning: A Practical Approach on the Statistical Learning Theory. Springer, 2018.
Publicado
07/11/2020
Como Citar
VOGADO, Luis H. S.; VERAS, Rodrigo M. S.; AIRES, Kelson R. T..
"LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 119-125.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.12993.
