2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 12 , Issue 2 , PP: 132-144, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Maruthi Prasad 1 * , Santhosh R. 2
Doi: https://doi.org/10.54216/FPA.120211
The studies’ primary aim is to help the research scholars as a source who would like to research in the thyroid disease detection region. UC Irvin knowledge discovery provides databases files for the machine learning archives' thyroid dataset. Here, a random vector network model (RVNM) is proposed to perform classification tasks. The proposed model integrates the prior dataset information regarding the samples to train the more effective classifier. This cascaded random vector network model helps in thyroid disease prediction. The evaluation process is performed to predict and determine the respective performance concerning accuracy. The intuition is provided in this research, like forecasting the thyroid disease; it also calls attention to the process of using a Randomized Vector Network Model (RVNM) as a medium for classification. The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches. The model gives a prediction accuracy of 96.1% accuracy compared to other models and shows a better trade than others.
Thyroid disease , classification , randomized vector , prediction accuracy , attention
[1] Kondo, K. Takagi, M. Nishida, T. Iwai, Y. Kudo, K. Ogawa et al., “Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with perflubutane micro-bubbles,” IEEE Transactions on Medical Imaging, vol. 36, no. 7, pp. 1427–1437, 2017
[2] Feng, F. Yang, X. Zhou, Y. Guo, F. Tang, F. Ren et al., “A deep learning approach for targeted contrast-enhanced ultrasound based prostate cancer detection,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 6, pp. 1794–1801, 2018.
[3] Rizzo, M. Tonietto, M. Castellaro, B. Raffeiner, A. Coran, U. Fiocco et al., “Bayesian quantification of contrast-enhanced ultrasound images with adaptive inclusion of an irreversible component,” IEEE Transac1tions on Medical Imaging, vol. 36, no. 4, pp. 1027–1036, 2017.
[4] Zhang, M. Ding, F. Meng, and X. Zhang, “Quantitative evaluation of two-factor analysis applied to hepatic perfusion study using contrast1enhanced ultrasound,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 2, pp. 259–267, 2013.
[5] Lueck, T. Kim, P. N. Burns, and A. L. Martel, “Hepatic perfusion imaging using factor analysis of contrast enhanced ultrasound,” IEEE Transactions on Medical Imaging, vol. 27, no. 10, pp. 1449–1457, 2008
[6] L. Guo, D. Wang, Y. Qian, X. Zheng, C. Zhao, X. Li et al., “A two1stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images,” Clinical Hemorheology and Microcirculation, vol. 69, no. 3, pp. 343–354, 2018.
[7] Guo, D. Wang, H. Xu, Y. Qian, C. Wang, X. Zheng et al., “CEUS1based classification of liver tumors with deep canonical correlation anal1ysis and multi-kernel learning,” in Proceedings of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, 2017, pp. 1748–1751.
[8] Wei, C. Zhang, L. Liu, C. Shen, and J. Wu, “Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification,” in Proceedings of the IEEE Conference on Asian Conference on Computer Vision, ACCV 2018, 2018, pp. 575–591.
[9] Rognin, M. Arditi, L. Mercier, P. J. Frinking, M. Schneider, G. Per1renoud et al., “Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 57, no. 11, pp. 2503–2511, 2010.
[10] Turco, P. Frinking, R. Wildeboer, M. Arditi, H. Wijkstra, J. R. Lindner et al., “Contrast-enhanced ultrasound quantification: From kinetic mod1eling to machine learning,” Ultrasound in Medicine Biology, vol. 46, pp. 518–543, 2020
[11] Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE Conference on International Conference on Computer Vision, ICCV 2015, 2015, pp. 4489–4497.
[12] Wang, C. Lian, D. Yao, D. Zhang, M. Liu, and D. Shen, “Spatial1temporal dependency modeling and network hub detection for functional mri analysis via convolutional-recurrent network,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 8, pp. 2241–2252, 2019.
[13] Zhang, X. He, X. Yu, W. Lu, Z. Zha, and Q. Tian, “A multi-scale spatial-temporal attention model for person re-identification in videos,” IEEE Transactions on Image Processing, vol. 29, pp. 3365–3373, 2020.
[14] Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural net1works,” in Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2018, 2018, pp. 7794–7803.
[15] Wang and A. Gupta, “Videos as space-time region graphs,” in Proceedings of the Computer Vision European Conference, ECCV 2018, vol. 11209, 2018, pp. 413–431.
[16] Lin, C. Gan, and S. Han, “TSM: temporal shift module for efficient video understanding,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2019, 2019, pp. 7082–7092.
[17] Zhang, X. Dai, and Y. Wang, “Dynamic temporal pyramid network: A closer look at multi-scale modeling for activity detection,” in Pro1ceedings of the Asian Conference on Computer Vision, ACCV 2018, vol. 11364, 2018, pp. 712–728.
[18] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904– 1916, 2015
[19] Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang et al., “Temporal segment networks for action recognition in videos,” CoRR, vol. abs/1705.02953, 2017.
[20] Zhang, Y.-X. Jiang, J.-B. Liu, M. Yang, Q. Dai, Q.-L. Zhu et al., “Utility of contrast-enhanced ultrasound for evaluation of thyroid nod1ules,” Thyroid, vol. 20, no. 1, pp. 51–57, 2010.
[21] Zhou, P. Zhou, Z. Hu, S. M. Tian, Y. Zhao, W. Liu et al., “Diagnostic efficiency of quantitative contrast-enhanced ultrasound indicators for discriminating benign from malignant solid thyroid nodules,” Journal of Ultrasound in Medicine, vol. 37, no. 2, pp. 425–437, 2018.
[22] He, X. Y. Wang, Q. Hu, X. X. Chen, B. Ling, and H. M. Wei, “Value of contrast-enhanced ultrasound and acoustic radiation force impulse imaging for the differential diagnosis of benign and malignant thyroid nodules,” Frontiers in pharmacology, vol. 9, p. 1363, 2018.
[23] Xuehua, L. Gao, Q. Wu, S. Fang, J. Xu, R. Liu et al., “Differentiation of thyroid nodules difficult to diagnose with contrast-enhanced ultra1sonography and real-time elastography,” Frontiers in oncology, vol. 10, p. 112, 2020.
[24] Luo, Y. Zhang, J. Yuan, X. Yang, L. Pang, L. Ding et al., “Differ1ential diagnosis of thyroid nodules through a combination of multiple ultrasonography techniques: A decision-tree model,” Experimental and therapeutic medicine, vol. 19, no. 6, pp. 3675–3683, 2020.
[25] S. Rinesh, K. Maheswari, B. Arthi, P. Sherubha, A. Vijay et al., “Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms,” Journal of Healthcare Engineering, Vol. 2, pp. 1-9, 2022.
[26] B. Narmadha P. Sherubha, M. Banu chitra, “Multi class feature selection algorithm for breast cancer detection”, International journal of pure and applied mathematics, pp. 301-305, 2018.
[27] P Sherubha, P Amudhavalli, SP Sasirekha, “clone attack detection using random forest and multi-objective cuckoo search classification”, International Conference on Communication and Signal Processing (ICCSP), pp. 0450-0454, 2019.
[28] Peng et al., “A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease,” Neurosci. Lett., vol. 651, pp. 88–94, 2017.
[29] Shi et al., “Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset,” Neurocomputing, vol. 194, pp. 87–94, 2016.
[30] Zheng et al., “Improving MRI-based diagnosis of alzheimer’s disease via an ensemble privileged information learning algorithm,” in Proc. 14th Int. Symp. Biomed. Imag., Melbourne, Australia, 2017, pp. 456–459.
[31] S. Hemamalini ,V. D. Ambeth Kumar ,R. Venkatesan,S. Malathi. (2023). Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification. Journal of Fusion: Practice and Applications, 11 ( 2 ), 90-110.
[32] C. S. Manigandaa,V. D. Ambeth Kumar,G. Ragunath,R. Venkatesan,N. Senthil Kumar. (2023). De-Noising and Segmentation of Medical Images using Neutrophilic Sets. Journal of Fusion: Practice and Applications, 11 ( 2 ), 111-123
[33] Sathya Preiya, V., and V. D. Ambeth Kumar. 2023. "Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes" Diagnostics 13, no. 12: 1983. https://doi.org/10.3390/diagnostics13121983
[34] Balakrishnan, Chitra, and V. D. Ambeth Kumar. 2023. "IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks" Diagnostics 13, no. 4: 775. https://doi.org/10.3390/diagnostics13040775.
[35] V. D. Ambeth Kumar,S. Malathi,Abhishek Kumar,Prakash M and Kalyana C. Veluvolu, “Active Volume Control in Smart Phones Based on User Activity and Ambient Noise” ,Sensors 2020, 20(15), 4117; https://doi.org/10.3390/s20154117
[36] V. Sathya Preiya,V. D. Ambeth Kumar,R. Vijay,Vijay K.,N. Kirubakaran. "Blockchain-Based E-Voting System with Face Recognition." Fusion: Practice and Applications, Vol. 12, No. 1, 2023 ,PP. 53-63
