Abstract
Cytogeneticists can diagnose various health and genetic disorders based on chromosome analysis. The standard technique in chromosome analysis is karyotyping, where each chromosome is classified into one of 24 chromosome classes. This process is performed manually inside the cytogenetics laboratory, and it consumes time, effort, and needs domain expertise. We automate in this paper a chromosome classification task by fine-tuning pre-trained convolutional neural networks models (VGG19, ResNet50, and MobileNetv2) and ensemble their results using majority voting and average voting. We compare the empirical performance for both ensemble methods on the biomedical imaging laboratory dataset that contains 5474 chromosome images which are publicly available online and on the diagnostic genomic medicine unit dataset that contains 6011 chromosome images. The best classification accuracy obtained on the biomedical imaging laboratory, and the diagnostic genomic medicine unit datasets was 97.01, 94.97%, respectively, when ensemble the results of the models by average voting.
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References
Munot MV, Joshi PM, Kulkarni P, Joshi MA (2012) Efficient pairing of chromosomes in metaphase image for automated karyotyping. In: 2012 IEEE-EMBS conference on biomedical engineering and sciences, pp 916–921
Faiyaz-Ul-Haque M (2011) Human Chromosomes Human Karyotype. Available https://slideplayer.com/slide/7949123/
Swati, Gupta G, Yadav M, Sharma M, Vig L (2017) Siamese networks for chromosome classification. In: 2017 IEEE international conference on computer vision workshops (ICCVW), pp 72–81
Lerner B (1998) Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans Syst Man Cybernet Part B (Cybernet) 28(4):544–552
Wang X, Zheng B, Li S, Mulvihill JJ, Wood MC, Liu H (2009) Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme. J Biomed Info 42(1):22–31
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Qin Y et al (2019) Varifocal-net: a chromosome classification approach using deep convolutional networks. IEEE Trans Med Imag 38(11):2569–2581
Abid F, Hamami L (2018) A survey of neural network based automated systems for human chromosome classification. Artif Intell Rev 49(1):41–56
Kheradpisheh SR, Ghodrati M, Ganjtabesh M, Masquelier T (2016) Deep networks can resemble human feed-forward vision in invariant object recognition,” (in eng). Sci Rep 6:32672–32672
Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?.In: Advances in neural information processing systems, pp 3320–3328
Marcelino P (2018) Transfer learning from pre-trained models. Available https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowled Data Eng 22(10):1345–1359
Rahman A, Tasnim S (2014) Ensemble classifiers and their applications: a review. arXiv preprint arXiv:1404.4088
Sagi O, Rokach L (2018) Ensemble learning: a survey. WIREs Data Mining and Knowledge Discovery 8(4):e1249
Pingel J (2019) Ensemble learning. Available https://blogs.mathworks.com/deep-learning/2019/06/03/ensemble-learning/
Yazdizadeh A, Patterson Z, Farooq B (2019) Ensemble convolutional neural networks for mode inference in smartphone travel survey
Poletti E, Grisan E, Ruggeri A (2008) Automatic classification of chromosomes in Q-band images. In: 2008 30th Annual international conference of the IEEE engineering in medicine and biology society, pp 1911–1914
Sharma M, Saha O, Sriraman A, Hebbalaguppe R, Vig L, Karande S (2017) Crowdsourcing for chromosome segmentation and deep classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 34–41
Sharma M, Vig L (2018) Automatic chromosome classification using deep attention based sequence learning of chromosome bands. In: 2018 International joint conference on neural networks (IJCNN), IEEE, pp 1–8
Zhang W et al (2018) Chromosome Classification with convolutional neural network based deep learning. In: 2018 11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1–5
Somasundaram D (2019) Machine learning approach for homolog chromosome classification. Int J Imaging Syst Technol 29(2):161–167
Swati S, Sharma M, Vig L (2019) Automatic classification of low-resolution chromosomal images. In: Computer vision–ECCV 2018 workshops, Cham, Springer International Publishing, pp 315–325
Leonardo MM, Carvalho TJ, Rezende E, Zucchi R, Faria FA (2018) Deep feature-based classifiers for fruit fly identification (Diptera: Tephritidae). In: 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), pp 41–47
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520
Raj B (2018, 28 Feb 2020). Data Augmentation| how to use deep learning when you have limited data—Part 2. Available https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced
Shorten C, Khoshgoftaar T (2019) A survey on image data augmentation for deep learning. J Big Data 6
(15/02/2020) Train deep learning network to classify new images. Available https://www.mathworks.com/help/deeplearning/examples/train-deep-learning-network-to-classify-new-images.html
C. o. E. I. G. M. R. (CEGMR) (2015) Cytogenetic Service Unit. Available https://cegmr.kau.edu.sa/Pages-Cytogenetic-Service-Unit-en.aspx
Bashmail R, Elrefaei LA, Alhalabi W (2018) Automatic segmentation of chromosome cells. In: Proceedings of the international conference on advanced intelligent systems and informatics 2018, Cham, Springer International Publishing, pp 654–663
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Al-Kharraz, M., Elrefaei, L.A., Fadel, M. (2021). Classifying Chromosome Images Using Ensemble Convolutional Neural Networks. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_58
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DOI: https://doi.org/10.1007/978-981-33-4604-8_58
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