An enhanced anarchic society optimization technique for the classification of ultrasound thyroid images using ILBP
Keywords:
anarchic society optimization, CAD, gray-level co-occurrence matrix, ILBP, KNNAbstract
In the recent times, Thyroid Nodules (TNs) is a generic cancer of the thyroid gland, which impacts close to 20% of the population worldwide and nearly 50% of 60- year-old individuals. The conventional diagnostic method, relying on the expertise of doctors, shows a huge drawback that the diagnosis result very much relies on the individual knowledge and experience of the physician. As a result, efficacy of diagnosis is confined, and it varies with the doctor’s experience. To combat this limitation, an efficient double screening technique is employed in few health care centers and hospitals by using one more specialist but, this approach is unaffordable and its time complexity is high. The research classified the thyroid nodules employing different image preprocessing techniques. Utilized histogram equalization for preprocessing in his work. The Gray-Level Co-Occurrence Matrix (GLCM) is deployed for extracting the significant features. The classification is done using ASO, k-Nearest Neighbor (KNN), and Bayesian. It is noticed that the ASO yields improved accuracy compared to KNN and Bayesian techniques.
Downloads
References
Acharya, U.R, Faust, O, Sree, S.V, Molinari, F and Suri, J. S, (2012). ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Computer methods and programs in biomedicine, vol. 107, no. 2, pp.233-241. DOI: https://doi.org/10.1016/j.cmpb.2011.10.001
Agarap, A.F, (2017). An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification, pp. 1-4.
Ahmadi-Javid, A, (2011). Anarchic Society Optimization: A human-inspired method. IEEE congress of evolutionary computation (CEC), pp. 2586-2592. DOI: https://doi.org/10.1109/CEC.2011.5949940
Albawi, S, Mohammed, T.A and Al-Zawi, S, (2017). Understanding of a convolutional neural network. International Conference on Engineering and Technology (ICET), pp. 1-6. DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186
Almufti, S.M, (2019). Historical survey on metaheuristics algorithms. International Journal of Scientific World, vol. 7, no. 1, pp.1-12. DOI: https://doi.org/10.14419/ijsw.v7i1.29497
Bibicu, D, Moraru, L and Biswas, A, (2013). Thyroid nodule recognition based on feature selection and pixel classification methods. Journal of Digital Imaging, vol. 26, no. 1, pp.119-128. DOI: https://doi.org/10.1007/s10278-012-9475-5
Bozorgi, A, Bozorg-Haddad, O and Chu, X, (2018). Anarchic society optimization (ASO) algorithm. In Advanced Optimization by Nature-Inspired Algorithms, pp. 31-38. DOI: https://doi.org/10.1007/978-981-10-5221-7_4
Buddhavarapu, V. G, (2020). An experimental study on classification of thyroid histopathology images using transfer learning. Pattern Recognition Letters, pp. 1-9. DOI: https://doi.org/10.1016/j.patrec.2020.09.020
Carraro, R, Molinari, F, Deandrea, M, Garberoglio, R and Suri, J.S, (2008). Characterization of thyroid nodules by 3-D contrast-enhanced ultrasound imaging. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2229-2232. DOI: https://doi.org/10.1109/IEMBS.2008.4649639
Cesareo, R, Palermo, A, Pasqualini, V, Manfrini, S, Trimboli, P. Stacul, F and Bernardi, S, (2020). Radio frequency ablation on autonomously functioning thyroid nodules: A critical appraisal and review of the literature. Frontiers in Endocrinology, pp. 1-6. DOI: https://doi.org/10.3389/fendo.2020.00317
Chang, C.Y, Hong, Y. C and Tseng, C.H, (2011). A neural network for thyroid segmentation and volume estimation in CT images. IEEE Computational Intelligence Magazine, vol. 6, no. 4, pp. 43-55. DOI: https://doi.org/10.1109/MCI.2011.942756
Chen, N, Klushyn, A, Kurle, R, Jiang, X, Bayer, J and Smagt, P, (2018). Metrics for deep generative models. International Conference on Artificial Intelligence and Statistics, pp. 1540-1550.
Chi, J, Walia, E, Babyn, P, Wang, J, Groot, G and Eramian, M, (2017). Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. Journal of Digital Imaging, vol. 30, no. 4, pp.477-486. DOI: https://doi.org/10.1007/s10278-017-9997-y
Cho, S.J, Baek, J.H, Chung, S.R, Choi, Y.J and Lee, J. H, (2020). Long-term results of thermal ablation of benign thyroid nodules: a systematic review and meta-analysis. Endocrinology and Metabolism, vol. 35, no. 2, pp. 339-350. DOI: https://doi.org/10.3803/EnM.2020.35.2.339
Dash, S and Senapati, M. R, (2018). Gray level run length matrix based on various illumination normalization techniques for texture classification. Evolutionary Intelligence, pp.1-10. DOI: https://doi.org/10.1007/s12065-018-0164-2
Ding, J, Cheng, H. D, Huang, J and Zhang, Y, (2014). Multiple-instance learning with global and local features for thyroid ultrasound image classification. IEEE International Conference on Biomedical Engineering and Informatics, pp. 66-70. DOI: https://doi.org/10.1109/BMEI.2014.7002744
Ding, J, Cheng, H.D, Huang, J, Zhang, Y and Ning, C, (2011). A novel quantitative measurement for thyroid cancer detection based on elastography. IEEE International Congress on Image and Signal Processing, vol. 4, pp. 1801-1804. DOI: https://doi.org/10.1109/CISP.2011.6100576
Dogantekin, E, Dogantekin, A and Avci, D, (2011). An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Systems with Applications, vol. 38, no. 1, pp. 146-150. DOI: https://doi.org/10.1016/j.eswa.2010.06.029
Dorigo, M and Stützle, T, (2019). Ant colony optimization: overview and recent advances. Handbook of Metaheuristics, pp. 311-351. DOI: https://doi.org/10.1007/978-3-319-91086-4_10
Frannita, E. L, Nugroho, H.A, Nugroho, A and Ardiyanto, I, (2018). Thyroid Nodule Classification Based on Characteristic of Margin using Geometric and Statistical Features. IEEE International Conference on Biomedical Engineering (IBIOMED), pp. 54-59. DOI: https://doi.org/10.1109/IBIOMED.2018.8534944
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
