Abstract
In the last decades, the classification of images was established as a typical method for diagnosing many abnormalities and diseases. The purpose of an efficient classification method is considered essential in modern diagnostic medicine in order to increase the number of diagnosed patients and decrease the analysis time. The significant storage capabilities of electronic media have enabled research centers to accumulate repositories of classified (labeled) images and mostly of a large number of unclassified (unlabeled) images. Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, seeing as they exploit the explicit classification information of labeled data with the knowledge hidden in the unlabeled data resulting in the creation of powerful and effective classifiers. In this work, we propose a new ensemble self-labeled algorithm, called DTCo, for X-ray classification. The efficacy of the presented algorithm is illustrated by a series of experiments against other state-of-the-art self-labeled methods.
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References
Alam J, Alam S, Hossan A (2018) Multi-stage lung cancer detection and prediction using multi-class svm classifier. In: 2018 international conference on computer, communication, chemical, material and electronic engineering, IEEE, pp 1–4
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: 11th annual conference on computational learning theory, pp 92–100
Candemir S, Jaeger S, Musco KPJ, Singh R, Xue Z, Karargyris A, Antani S, Thoma G, McDonald C (2014) Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging 33:577–590
Hady M, Schwenker F (2010) Combining committee-based semi-supervised learning and active learning. J Comput Sci Technol 25(4):681–698
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I (2009) The WEKA data mining software: an update, SIGKDD explorations newsletters 11:10–18
Hogeweg L, Mol C, de Jong P, Ayles R, van Ginneken B (2010) Fusion of local and global detection systems to detect tuberculosis in chest radiographs. In: Medical image computing and computer-assisted intervention, pp 650–657. Springer, Berlin
Hogeweg L, Sanchez C, de Jong P, Maduskar P, van Ginneken B (2012) Clavicle segmentation in chest radiographs. Med Image Anal 16(8):1490–1502
Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh R, Antani S, Thoma G, Wang Y, Lu P, McDonald C (2014) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 33:233–245
Kermany D, Goldbaum M, Cai W, Valentim C, Liang H, Baxter S, McKeown A, Yang G, Wu X, Yan F (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131
Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23(1):89–109
Li M, Zhou Z (2007) Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans Syst Man Cybern Part A Syst Hum 37(6):1088–1098
Livieris IE (2019) A new ensemble self-labeled semi-supervised algorithm. Informatica:1–14, (to be appear)
Livieris IE, Kanavos A, Tampakas V, Pintelas P (2018) An ensemble SSL algorithm for efficient chest X-ray image classification. J Imaging 4(7)
Muyoyeta M, Maduskar P, Moyo M, Kasese N, Milimo D, Spooner R, Kapata N, Hogeweg L, van Ginneken B, Ayles H (2014) The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with xpert mtb/rif in lusaka zambia. PLoS One 9(4):e93757
Plankis T, Juozapavicius A, Stasiene E, Usonis V (2017) Computer-aided detection of interstitial lung diseases: a texture approach. Nonlinear Anal 22(3):404–411
Santosh K, Antani S (2018) Automated chest X-ray screening: can lung region symmetry help detect pulmonary abnormalities? IEEE Trans Med Imaging 37(5):1168–1177
Stirenko S, Kochura Y, Alienin O, Rokovyi O, Gang P, Zeng W, Gordienko Y (2018) Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation, arXiv preprint arXiv:1803.01199
Triguero I, GarcÃa S, Herrera F (2015) Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl Inf Syst 42(2):245–284
van Ginneken B, Hogeweg L, Prokop M (2009) Computer-aided diagnosis in chest radiography: beyond nodules. Eur J Radiol 72(2):226–230
World Health Organization (2017) Global tuberculosis report 2017
Xu T, Cheng I, Mandal M (2011) Automated cavity detection of infectious pulmonary tuberculosis in chest radiographs. In: IEEE international conference on engineering in medicine and biology society, pp 5178–5181
Yarowsky D (1995) Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd annual meeting of the association for computational linguistics, pp 189–196
Zhou Z (2011) When semi-supervised learning meets ensemble learning, vol 6. Springer, pp 6–16. Springer, Berlin
Zhou Y, Goldman S (2004) Democratic co-learning. In: 16th IEEE international conference on tools with Artificial Intelligence (ICTAI), IEEE, pp 594–602
Zhou Z, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529–1541
Zhu X (2011) Semi-supervised learning. In: Encyclopedia of machine learning. Springer, pp 892–897
Zhu X, Goldberg A (2009) Introduction to semi-supervised learning, In: Synthesis lectures on artificial intelligence and machine learning 3(1), pp 1–130
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Livieris, I., Kotsilieris, T., Anagnostopoulos, I., Tampakas, V. (2020). DTCo: An Ensemble SSL Algorithm for X-ray Classification. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_24
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DOI: https://doi.org/10.1007/978-3-030-32622-7_24
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