The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection
Tóm tắt
Từ khóa
Tài liệu tham khảo
World Health Organization. Coronavirus disease (COVID-19) pandemic (2020). https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
Radiology Assistant. X-ray Chest images (2020). https://radiologyassistant.nl/chest/lk-jg-1. Accessed 23 Mar 2020.
Wei J, Hagihara Y, Shimizu A, Kobatake H. Optimal image feature set for detecting lung nodules on chest X-ray images. CARS 2002 computer assisted radiology and surgery. Berlin: Springer; 2002. p. 706–711.
Schilham AM, van Ginneken B, Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med Image Anal. 2006;10(2):247–58.
Woźniak M, Połap D, Capizzi G, Sciuto GL, Kośmider L, Frankiewicz K. Small lung nodules detection based on local variance analysis and probabilistic neural network. Comput Methods Programs Biomed. 2018;161:173–80.
Ho TKK, Gwak J. Multiple feature integration for classification of thoracic disease in chest radiography. Appl Sci. 2019;9(19):4130.
Noor NM, Rijal OM, Yunus A, Mahayiddin AA, Peng GC, Abu-Bakar SAR. A statistical interpretation of the chest radiograph for the detection of pulmonary tuberculosis. In: 2010 IEEE EMBS conference on biomedical engineering and sciences (IECBES), Kuala Lumpur, Malaysia. IEEE. 2010. pp. 47–51. https://doi.org/10.1109/iecbes.2010.5742197.
Xu T, Cheng I, Long R, Mandal M. Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs. EURASIP J Image Video Process. 2013;2013(1):3.
Plankis T, Juozapavicius A, Stasiene E, Usonis V. Computer-aided detection of interstitial lung diseases: a texture approach. Nonlinear Anal Model. 2017;22(3):404–11.
Kumar A, Yen-Yu W, Kai-Che L, Tsai IC, Ching-Chun H, Nguyen H. Distinguishing normal and pulmonary edema chest X-ray using Gabor filter and SVM. In: 2014 IEEE international symposium on bioelectronics and bioinformatics (IEEE ISBB 2014). IEEE, Chung Li, Taiwan, 2014. pp. 1–4. https://doi.org/10.1109/isbb.2014.68209.18.
Kesim E, Dokur Z, Olmez T. X-ray chest image classification by a small-sized convolutional neural network. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). IEEE (2019). https://doi.org/10.1109/EBBT.2019.8742050.
Liu C, Cao Y, Alcantara M, Liu B, Brunette M, Peinado J, Curioso W. TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network. In: 2017 IEEE international conference on image processing (ICIP), pp. 2314–2318. IEEE (2017).
Xu S, Wu H, Bie R. CXNet-m1: anomaly detection on chest X-rays with image-based deep learning. IEEE Access. 2018;7:4466–77.
Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686.
Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins D, Raja E. Deep-learning framework to detect lung abnormality: a study with chest X-ray and lung CT scan images. Pattern Recogn Lett. 2020;129:271–8.
Ucar F, Korkmaz D. COVIDiagnosis-net: deep Bayes-Squeeze net based diagnostic of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypoth. 2020;140:109761.
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792.
GitHub. COVID-19. (2020). https://github.com/ieee8023/covid-chestxray-dataset/tree/master/images. Accessed 10 Mar 2020.
Kaggle. Covid-19 X-ray chest and CT. (2020a).https://www.kaggle.com/bachrr/covid-chest-xray. Accessed 20 Apr 2020.
Kaggle. X-ray chest. (2020b).https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 10 Mar 2020.
Antonini M, Barlaud M, Mathieu P, Daubechies I. Image coding using wavelet transform. IEEE Trans Image Process. 1992;1(2):205–20.
Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process. 2005;14(12):2091–106.
Easley G, Labate D, Lim WQ. Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal. 2008;25(1):25–46.
Sengur A, Turkoglu I, Ince MC. Wavelet packet neural networks for texture classification. Expert Syst Appl. 2007;32(2):527–33.
Alcin OF, Sengur A, Ghofrani S, Ince MC. GA-SELM: Greedy algorithms for sparse extreme learning machine. Measurement. 2014;55:126–32.
Häuser S, Steidl G. Fast finite shearlet transform (2012). arXiv preprint arXiv:1202.1773.
Omar N, Sengur A, Al-Ali SGS. Cascaded deep learning-based efficient approach for license plate detection and recognition. Expert Syst Appl. 2020;149:113280.
Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1–3):489–501.
Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Health Inform Sci Syst. 2018;6(1):18.
He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. European conference on computer vision. Cham: Springer; 2016. p. 630–645.