Abstract
Chromosomes are bodies that contain human genetic information. Chromosomal disorders can cause structural and functional disorders in individuals. Detecting the metaphase stages of the cells accurately is a crucial step to detect possible defects in chromosomes. Thus, it is vital at this stage to identify the identical chromosome of each chromosome, to perform the pairing process, and to identify problems arising from this process. In this study, it was investigated whether the analyzable metaphase images can be analyzed by using the transfer learning and fine tuning approaches of deep learning models. The weights of VGG16 and InceptionV3 models trained with ImageNet data set were transferred to this problem and the classification process was carried out. True positive ratio values are 99%(± 0.9) and 99%(± 0.9) for VGG and Inception networks, respectively. The classification performances obtained depending on the changing training set ratios are presented comparatively in figures. F-measure, precision, and recall values obtained for the VGG and Inception networks were observed as 99%(± 1.0) and 99%(± 1.0), respectively. F-measure, precision, and recall values of VGG and Inceptionv3 networks are also presented with respect to the ratio of training size. The obtained results have compared with the state-of-the-art methods in the literature and supported with the tables and graphics. The training phase was also accelerated by using transfer learning and fine tuning methods. Transfer learning and fine tuning processes have almost similar performance as the models used in the literature and trained from scratch in metaphase
detection.
Similar content being viewed by others
References
Albayrak A, Bilgin G (2016) Mitosis detection using convolutional neural network based features. In: 2016 IEEE 17Th international symposium on computational intelligence and informatics (CINTI). IEEE, pp 000335–000340
Arora T, Dhir R (2016) A review of metaphase chromosome image selection techniques for automatic karyotype generation. Med Biol Eng Comp 54(8):1147–1157
Castleman KR (1992) The psi automatic metaphase finder. J Radiat Res 33(Suppl_1):124–128
Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525
Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M (2017) Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 30(4):477–486
Dai X, Huang L, Qian Y, Xia S, Chong W, Liu J, Di Ieva A, Hou X, Ou C (2020) Deep learning for automated cerebral aneurysm detection on computed tomography images. Int j Comput Assist Radiol Surg 1–9
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition. Ieee, pp 248–255
Deng L, Yu D (2014) Deep learning: methods and applications. Founds Trends Signal Process 7(3–4):197–387
Furukawa A, Minamihisamatsu M, Hayata I (2010) Low-cost metaphase finder system. Health Phys 98(2):269–275
Garza-Jinich M, Rodriguez C, Corkidi G, Montero R, Rojas E, Ostrosky-Wegman P (1992) A microcomputer-based supervised system for automatic scoring of mitotic index in cytotoxicity studies. In: Advances in machine vision: Strategies and applications. World Scientific, pp 301–311
Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A (2017) Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr Build Mater 157:322–330
Jahani S, Setarehdan SK, Fatemizadeh E (2011) Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators. In: 2011 7Th iranian conference on machine vision and image processing. IEEE, pp 1–4
Jia Y, Zhang Y, Weiss R, Wang Q, Shen J, Ren F, Nguyen P, Pang R, Moreno IL, Wu Y (2018) Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In: Advances in neural information processing systems, pp 4480–4490
Kainulainen K, Pulkkinen L, Savolainen A, Kaitila I, Peltonen L (1990) Location on chromosome 15 of the gene defect causing marfan syndrome. N Engl J Med 323(14):935–939
Korenberg JR, Chen X, Schipper R, Sun Z, Gonsky R, Gerwehr S, Carpenter N, Daumer C, Dignan P, Disteche C (1994) Down syndrome phenotypes: the consequences of chromosomal imbalance. Proc Natl Acad Sci 91(11):4997–5001
Korenberg JR, Freedlender EF (1974) Giemsa technique for the detection of sister chromatid exchanges. Chromosoma 48(4):355–360
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P., Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740–755
Liu D, Yu J (2009) Otsu method and k-means. In: 2009 Ninth international conference on hybrid intelligent systems, vol 1. IEEE, pp 344–349
McLean J, Johnson F (1995) Evaluation of a metaphase chromosome finder: potential application to chromosome-based radiation dosimetry. Micron 26(6):489–492
Moazzen Y, Çapar A., Albayrak A, Çalık N, Töreyin BU (2019) Metaphase finding with deep convolutional neural networks. Biomed Signal Process and Control 52:353–361
Munot M, Joshi M, Sharma N (2011) Automated karyotyping of metaphase cells with touching chromosomes. Int J Comput Appl 29(12):14–20
Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1
Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359
Qiu Y (2013) Comprehensive performance evaluation and optimization of high throughput scanning microscopy for metaphase chromosome imaging
Ruder S, Peters ME, Swayamdipta S, Wolf T (2019) Transfer learning in natural language processing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pp 15–18
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
St George-Hyslop PH, Tanzi RE, Polinsky RJ, Haines JL, Nee L, Watkins PC, Myers RH, Feldman RG, Pollen D, Drachman D (1987) The genetic defect causing familial alzheimer’s disease maps on chromosome 21. Science 235(4791):885–890
Subasinghe A, Samarabandu J, Li Y, Wilkins R, Flegal F, Knoll JH, Rogan PK (2016) Centromere detection of human metaphase chromosome images using a candidate based method. F1000Research 5
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Tjio JH, Levan A (1956) The chromosome number of man. Hereditas 42(1-2):1–6
Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI global, pp 242–264
Uttamatanin R, Yuvapoositanon P, Intarapanich A, Kaewkamnerd S, Phuksaritanon R, Assawamakin A, Tongsima S (2013) Metasel: a metaphase selection tool using a gaussian-based classification technique. BMC bioinformatics 14(16):S13
Uttamatanin R, Yuvapoositanon P, Intarapanich A, Kaewkamnerd S, Tongsima S (2013) Chromosome classification for metaphase selection. In: 2013 13Th international symposium on communications and information technologies (ISCIT). IEEE, pp 464–468
Vijayakumar S, D’souza A, Schaal S (2005) Incremental online learning in high dimensions. Neural Comput 17(12):2602–2634
Vrolijk J, Sloos W, Darroudi F, Natarajan A, Tanke H (1994) A system for fluorescence metaphase finding and scoring of chromosomal translocations visualized by in situ hybridization. Int J Radiat Biol 66(3):287–295
Wang X, Li S, Liu H, Wood M, Chen WR, Zheng B (2008) Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J of Biomed Inform 41(2):264–271
Yan W (2009) A counting algorithm for overlapped chromosomes. In: 2009 3Rd international conference on bioinformatics and biomedical engineering. IEEE, pp 1–3
Yilmaz H, Turan MK (2017) Fahamecv1: a low cost automated metaphase detection system. Eng Technol Appl Sci Res 7(6):2160–2166
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Albayrak, A. Classification of analyzable metaphase images using transfer learning and fine tuning. Med Biol Eng Comput 60, 239–248 (2022). https://doi.org/10.1007/s11517-021-02474-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-021-02474-z