References
- Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. 2020. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 296(2):E32–E40. doi:https://doi.org/10.1148/radiol.2020200642.
- Akselrod-Ballin A, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R, Barkan E, 2016. A region based convolutional network for tumor detection and classification in breast mammography. In Lect notes comput sci (including subser lect notes artif intell lect notes Bioinformatics). Vol. 10008. Springer, Cham: LNCS. https://doi.org/https://doi.org/10.1007/978-3-319-46976-8_21.
- Altan A, Karasu S. 2020. Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals. 140:110071. doi:https://doi.org/10.1016/j.chaos.2020.110071.
- Andrearczyk V, Whelan PF. 2016. Using filter banks in convolutional neural networks for texture classification. Pattern Recognit Lett. 84:63–69. doi:https://doi.org/10.1016/j.patrec.2016.08.016.
- Apostolopoulos ID, Aznaouridis SI, Tzani MA. 2020. Extracting possibly representative COVID-19 Biomarkers from X-ray images with deep learning approach and image data related to Pulmonary diseases. J Med Biol Eng. 40(3):462–469. doi:https://doi.org/10.1007/s40846-020-00529-4.
- Backes AR, De Mesquita Sá Junior JJ. 2017. LBP maps for improving fractal based texture classification. Neurocomputing. 266:1–7. doi:https://doi.org/10.1016/j.neucom.2017.05.020.
- Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Pan I, Shi LB, Wang DC, Mei J, et al. 2020. Performance of radiologists in differentiating COVID-19 from Non-COVID-19 viral pneumonia at Chest CT. Radiology. 296(2). https://doi.org/https://doi.org/10.1148/radiol.2020200823
- Basu S, Mukhopadhyay S, Karki M, DiBiano R, Ganguly S, Nemani R, Gayaka S. 2018. Deep neural networks for texture classification—A theoretical analysis. Neural Networks. 97:173–182. doi:https://doi.org/10.1016/j.neunet.2017.10.001.
- Chan CH, Huang TT, Chen CY, Lee CC, Chan MY, Chung PC. 2019. Texture-map-based branch-collaborative network for oral cancer detection. IEEE Trans Biomed Circuits Syst. 13(4):766–780. doi:https://doi.org/10.1109/TBCAS.2019.2918244.
- Chau CH, Strope JD, Figg WD. 2020. COVID-19 clinical diagnostics and testing technology. Pharmacotherapy. 40(8):857–868. doi:https://doi.org/10.1002/phar.2439.
- Chen X, Yao G, Cai J, Huang Y, Yuan X. 2017. Fractal and multifractal analysis of different hydraulic flow units based on micro-CT images. J Nat Gas Sci Eng. 48:145–156. doi:https://doi.org/10.1016/j.jngse.2016.11.048.
- Cheng X, Zhang L, Zheng Y. 2018. Deep similarity learning for multimodal medical images. Comput Methods Biomech Biomed Eng Imaging Vis [Internet]. 6(3):248–252. https://www.tandfonline.com/doi/full/10.1080/21681163.2015.1135299
- Das A, Acharya UR, Panda SS, Sabut S. 2019. Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn Syst Res. 54:165–175. doi:https://doi.org/10.1016/j.cogsys.2018.12.009.
- Das S, Mishra S, Prasad S, Senapati MR. 2015. A harmony search-based artificial neural network for stock market prediction. Int J Bus Forecast Mark Intell. 2(1):19–36. https://doi.org/https://doi.org/10.1504/ijbfmi.2015.075323
- Dash S, Mr S, Jena UR. 2018. K-NN based automated reasoning using bilateral filter based texture descriptor for computing texture classification. Egypt Informatics J [Internet]. accessed 2018 Dec 10. 19(2):133–144. https://www.sciencedirect.com/science/article/pii/S111086651730110X
- Dong D, Tang Z, Wang S, Hui H, Gong L, Lu Y, Xue Z, Liao H, Chen F, Yang F, et al. 2021. The role of imaging in the detection and management of COVID-19: a review. IEEE Rev Biomed Eng,14:16-29. https://doi.org/10.1109/RBME.2020.2990959.
- Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. 2020. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 296(2):E115–E117. doi:https://doi.org/10.1148/radiol.2020200432.
- Gao M, Bagci U, Lu L, Wu A, Buty M, Shin H-C, Roth H, Papadakis GZ, Depeursinge A, Summers RM, et al. 2018. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis [Internet]. 6(1):1–6. https://www.tandfonline.com/doi/full/10.1080/21681163.2015.1124249
- Grau J, Méndez V, Tarquis AM, Díaz MC, Saa A. 2006. Comparison of gliding box and box-counting methods in soil image analysis. Geoderma. 134(3–4):3–4. doi:https://doi.org/10.1016/j.geoderma.2006.03.009.
- Haralick RM, Dinstein I, Shanmugam K. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. SMC-3(6):610–621. doi:https://doi.org/10.1109/TSMC.1973.4309314.
- J. Cojocaru, D. Popescu and L. Ichim, “Color Texture Classification Combining LBP Images and Fractal Features,” 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2019, pp. 35–40, doi:https://doi.org/10.1109/CIS-RAM47153.2019.909554.
- Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 121:103792. doi:https://doi.org/10.1016/j.compbiomed.2020.103792.
- Polsinelli M, Cinque L, Placidi G. 2020. A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognit Lett. 140:95–100. doi:https://doi.org/10.1016/j.patrec.2020.10.001.
- Ranganath A, Mishra J. 2017. New approach for estimating fractal dimension of both gary and color images. In: 2017 IEEE 7th Int Adv Comput Conf [Internet]; [place unknown]; IEEE [accessed 2018 Jul 31]; p. 678–683. http://ieeexplore.ieee.org/document/7976876/. doi: https://doi.org/10.1109/IACC.2017.0142.
- Ranganath A, Senapati MR, Sahu PK. 2020. Estimating the fractal dimension of images using pixel range calculation technique. Vis Comput [Internet]. http://link.springer.com/10.1007/s00371-020-01829-1
- Rastghalam R, Pourghassem H. 2016. Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images. Pattern Recognit. 51:176–186. doi:https://doi.org/10.1016/j.patcog.2015.09.009.
- Saba T, Sameh Mohamed A, El-Affendi M, Amin J, Sharif M. 2020. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res. 59:221–230. doi:https://doi.org/10.1016/j.cogsys.2019.09.007.
- SARS-COV-2 Ct-scan dataset. Kaggle. https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset
- Selvapandian A, Manivannan K. 2018. Fusion based Glioma brain tumor detection and segmentation using ANFIS classification. Comput Methods Programs Biomed. 166:33–38. doi:https://doi.org/10.1016/j.cmpb.2018.09.006.
- Shahabaz, Somwanshi, Devendra, Yadav, Ashwani, Roy and Ratnadeep. 2017. Medical images texture analysis: A review. 436–441. https://doi.org/10.1109/COMPTELIX.2017.8004009.
- Singh R, Goel A, Raghuvanshi DK. 2020. Computer-aided diagnostic network for brain tumor classification employing modulated Gabor filter banks. Vis Comput. 2157–2171. https://doi.org/https://doi.org/10.1007/s00371-020-01977-4
- Tripathi S, Verma A, Sharma N. 2021. Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach. Comput Methods Biomech Biomed Eng Imaging Vis [Internet]. 9(2):121–130. https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1818628
- Tuncer T, Dogan S, Ozyurt F. 2020. An automated residual exemplar local binary pattern and iterative relieff based Corona detection method using lung X-ray image. Chemom Intell Lab Syst. 203:104054. doi:https://doi.org/10.1016/j.chemolab.2020.104054.
- Varela-Santos S, Melin P. 2021. A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf Sci (Ny). 545:403–414. doi:https://doi.org/10.1016/j.ins.2020.09.041.
- Wang Q, Zheng Y, Yang G, Jin W, Chen X, Yin Y. 2018. Multiscale rotation-invariant convolutional neural networks for lung texture classification. IEEE J Biomed Heal Informatics. 22(1):184–195. doi: https://doi.org/10.1109/JBHI.2017.2685586.
- Z. Guo, L. Zhang, D. Zhang and S. Zhang, “Rotation invariant texture classification using adaptive LBP with directional statistical features,” 2010 IEEE International Conference on Image Processing, 2010, pp. 285–288.https://doi.org/10.1109/ICIP.2010.5652209
- Zhao HH, Rosin PL, Lai YK, Wang YN. 2020. Automatic semantic style transfer using deep convolutional neural networks and soft masks. Vis Comput. 36(7):1307–1324. doi:https://doi.org/10.1007/s00371-019-01726-2.
- Zuluaga-Gomez J, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N. 2021. A CNN-based methodology for breast cancer diagnosis using thermal images. Comput Methods Biomech Biomed Eng Imaging Vis [Internet]. 9(2):131–145. https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1824685