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Information Engineering

A benchmarking platform for selecting optimal retinal diseases diagnosis model based on a multi-criteria decision-making approach

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Pages 27-34 | Received 02 Feb 2021, Accepted 27 Aug 2021, Published online: 27 Oct 2021

References

  • Abdurrahman, H., and M. Khatib. 2020. “Classification Retina Diseases from OCT Using Genetic Programming.” International Journal of Computer Applications 117 (45): 41–46. doi:https://doi.org/10.5120/ijca2020919973.
  • Alinezhad, A., and J. Khalili. 2019. New Methods and Applications in Multiple Attribute Decision Making (MADM). New York: Springer.
  • Alper, D., and C. Basdar. 2017. “Comparison of TOPSIS and ELECTRE Methods: An Application on the Factoring Industry.” Business and Economics Research Journal 8 (3): 627–646. doi:https://doi.org/10.20409/berj.2017.70.
  • Awais, M., H. Műller, T. B. Tang, and F. Mériaudeau 2017. “Classification of SD-OCT Images Using a Deep Learning Approach.” In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Malaysia, 12-14 September 2017: 489–492. New York: IEEE.
  • Behazadian, M., S. K. Otaghsara, M. Yazdani, and J. Ignatius. 2012. “A State-of-the-Art Survey of TOPSIS Applications.” Expert Systems with Applications 39 (17): 13051–13069. doi:https://doi.org/10.1016/j.eswa.2012.05.056.
  • Bulgurcu, B. 2012. “Application of TOPSIS Technique for Financial Performance Evaluation of Technology Firms in Istanbul Stock Exchange Market.” Procedia - Social and Behavioral Sciences 62: 1033–1040. doi:https://doi.org/10.1016/j.sbspro.2012.09.176.
  • Chan, G. C. Y., R. Kamble, H. Műller, S. A. A. Shah, T.B Tang, and F. Mériaudeau 2018. “Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.” In 2018 40th Annual International Conference of IEEE Engineering Medical Biological Society (EMBC), Honolulu, HI, USA, 18-21 July 2018: 670–673. New York: IEEE.
  • Chetoui, M., and M. A. Akhloufi. 2020. “Deep Retinal Diseases Detection and Explainability Using OCT.” In International Conference on Image Analysis and Recognition (ICIAR), Póvoa de Varzim, Portugal, 24-26 June 2020: 358–366. New York: Springer.
  • Chodha, V., R. Dubey, R. Kumar, S. Singh, and S. Kaur. 2021. “Selection of Industrial Arc Welding Robot with TOPSIS and Entropy MCDM Techniques.” Materials Today: Proceedings. doi:https://doi.org/10.1016/j.matpr.2021.04.487.
  • Dong, J., S. Wan, and S. M. Chen. 2021. “Fuzzy Best-Worst Method Based on Triangular Fuzzy Numbers for Multi-Criteria Decision-Making.” Information Sciences 547: 1080–1104. doi:https://doi.org/10.1016/j.ins.2020.09.014.
  • Feng, L., H. Chen, Z. Liu, X.-D. Zhang, M.-S. Jiang, Z.-Z. Wu, and K. Q. Zhou. 2019. “Deep Learning-Based Automated Detection of Retinal Diseases Using Optical Coherence Tomography Images.” Biomedical Optics Express 10 (12): 6204–6226. doi:https://doi.org/10.1364/BOE.10.006204.
  • Hussain, M. A., A. Bhuiyan, C. D. Luu, R. T. Smith, R.H. Guymer, H. Ishikawa, J.S. Schuman, K. Ramamohanarao, and D G. Vavvas. 2018. “Classification of Healthy and Diseased Retina Using SD-OCT Imaging and Random Forest Algorithm.” PLoS ONE 13 (6): e0198281-1-e0198281-17. doi:https://doi.org/10.1371/journal.pone.0198281.
  • Kermany, D. S., M. Goldbaum, W. Cai, C.C.S. Valentim, H. Liang, S. L. Baxter, A. McKeown, et al. 2018. “Identifying Medical Diagnosis and Treatable Diseases by Image-Based Deep Learning.” Cell 172 (5): 1122–1131. doi:https://doi.org/10.1016/j.cell.2018.02.010.
  • Li, H., J. Huang, Y. Hu, S. Wang, J. Liu, and L. Yang. 2021. “A New TMY Generation Method Based on the Entropy-Based TOPSIS Theory for Different Climatic Zones in China.” Energy 231: 120723. doi:https://doi.org/10.1016/j.energy.2021.120723.
  • Li, X., K. Wang, L. Liu, J. Xin, H. Yang, and C. Gao. 2011. “Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines.” Procedia Engineering 26: 2085–2091. doi:https://doi.org/10.1016/j.proeng.2011.11.2410.
  • Mardani, A., A. Jusoh, K. M. Nor, Z. Khalifah, N. Zakwan, and A. Valipour. 2015. “Multiple Criteria Decision-Making Techniques and Their Applications - A Review of the Literature from 2000 to 2014.” Economic Research 28 (1): 516–571. doi:https://doi.org/10.1080/1331677X.2015.1075139.
  • Mohammed, M. A., K. H. Abdulkareem, A. S. Al-Waisy, S A. Mostafa, S. Al-Fahdawi, A M. Dinar, W. Alhakami, et al. 2020. “Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods.” IEEE Access 8 :99115–99131. doi:https://doi.org/10.1109/ACCESS.2020.2995597.
  • Nabil, M., N. Saleh, M. A. Eldosoky, and A. M. Soliman. 2020. “ An Automated Evaluation System for Medical Equipment Based on Standardization”. In 2020 12th International Conference on Electrical Engineering (ICEENG), Dhaka, Bangladesh, 7-9 July 2020: 129–134. New York: IEEE.
  • Najeeb, S., N. Sharmile, M. S. Khan, I. Sahin, M. T. Islam, and M. I. H. Bhuiyan. 2018. “Classification of Retinal Diseases from OCT Scans Using Convolutional Neural Networks”. In 2018 10th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 20-22 December 2018. New York: IEEE.
  • Odu, G. O. 2019. “Weighting Methods for Multi-Criteria Decision Making Technique.” Journal of Applied Sciences and Environmental Management 23 (8): 1449–1457. doi:https://doi.org/10.4314/jasem.v23i8.7.
  • Rajagopalan, N. V., Narasimhan, S. Kunnavakkam Vinjimoor, J. Aiyer, and J. Aiyer. 2020. “Deep CNN Framework for Retinal Disease Diagnosis Using Optical Coherence Tomography Images.” Journal of Ambient Intelligence and Humanized Computing 12 (7): 7569–7580. doi:https://doi.org/10.1007/s12652-020-02460-7.
  • Sertkaya, M. E., B Ergen, and M. Togacar. 2019. “Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images.” In 2019 23rd International Conference Electronics. Palanga, Lithuania, 17-19 June 2019: 1–5. New York: IEEE.
  • Skansi, S. 2018. Introduction to Deep Learning from Logical Calculus to Artificial Intelligence. New York: Springer.
  • Vujičić, M. D., M. Z. paić, and M. D. Blagojević. 2017. “Comparative Analysis of Objective Techniques for Criteria Weighing in Two MCDM Methods on Example of an Air Conditioner Selection.” Tehnika 67 (3): 422–429. doi:https://doi.org/10.5937/tehnika1703422V.
  • Wan, S., and J. Dong. 2021. “A Novel Extension of Best-Worst Method with Intuitionistic Fuzzy Reference Comparisons.” IEEE Transactions on Fuzzy Systems 1. doi:https://doi.org/10.1109/TFUZZ.2021.3064695.
  • Wang, Z., and W. Zhan. 2012. “Dynamic Engineering Multi-Criteria Decision Making Model Optimized by Entropy Weight for Evaluating Bid.” Systems Engineering Procedia 5: 49–54. doi:https://doi.org/10.1016/j.sepro.2012.04.008.
  • Zhu, W., S. Liu, H. Xie, and F. Huang. 2020. “An Online Evaluation Method Based on Entropy-Topsis Radar Interference Effect.” In 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 27-29 June 2020: 606–609. New York: IEEE.
  • Zhu, Y., D. Tian, and F. Yan. 2020. “Effectiveness of Entropy Weight Method in Decision-Making.” Mathematical Problems in Engineering 2020: 3564835-1-3564835-5. doi:https://doi.org/10.1155/2020/3564835.

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