Abstract
Case based reasoning (CBR) is one of the most widely used reasoning approaches in expert knowledge-centered domains such as the medical sector, due to the risk that a false diagnosis may generate the requirement for models to be explainable. However, these models depend considerably on the user input (symptoms) and the input of the patient’s radiology image. Deep learning approaches based on convolutional neural networks have been proved in several papers to be relevant in imaging processing. This work proposes a hybrid framework of case-based reasoning and deep learning to be applied to a diagnostic support system. In the paper, we propose to couple the power of CNNs in radiology image analysis with the user-centered approach of case-based reasoning models. In addition, the framework on which we based is modular and adapts to a wide variety of tasks, data and uses, in fact, the system will receive as input the radiological image of the patient combined with the different symptoms, and will propose to the experts one or more predicted diagnoses with a probability score for each. The expert can validate or not a choice, his decisions will be taken in charge by the system to be added to the knowledge base in the case of validation or stored in another separate base in the case of non-validation. An implementation of the approach for diagnostic support is provided.
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References
COVER, P. E. H. T.M.: Nearest neighbor pattern classfication, vol. I, pp. 1–28 (2012)
Jedwabny, M., Bisquert, P., Croitoru, M.: Probabilistic rule induction for transparent CBR under uncertainty. In: Bramer, M., Ellis, R. (eds.) SGAI-AI 2021. LNCS (LNAI), vol. 13101, pp. 117–130. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91100-3_9
Gao, L., Liu, C., Arefan, D., Panigrahy, A., Zuley, M.L., Wu, S.: Medical knowledge-guided deep learning for imbalanced medical image classification (2021). http://arxiv.org/abs/2111.10620
Gonzalez-Ferrer, A., Seara, G., Cháfer, J., Mayol, J.: Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare. Int. J. Interact. Multimed. Artif. Intell. 4(7), 42 (2018). https://doi.org/10.9781/ijimai.2017.03.006
Amador-Domínguez, E., Serrano, E., Manrique, D., Bajo, J.: A case-based reasoning model powered by deep learning for radiology report recommendation. Int. J. Interact. Multimed. Artif. Intell. 7(2), 15–26 (2021). https://doi.org/10.9781/ijimai.2021.08.011
Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N., Kroeker, K.I.: An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit. ed. 3(1), 1 (2020). https://doi.org/10.1038/s41746-020-0221-y
Sànchez-marrè, M.: Principles of case-based reasoning. In: Cycle, pp. 1–13 (1994)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, method ological variations, and system approaches. Artif. Intell. Commun. 7(1), 39–59 (1996). https://ibug.doc.ic.ac.uk/media/uploads/documents/courses/CBR-AamodtPlaza.pdf
De Mántaras, R., McSherry, D., Bridge, D.: Retrieval, reuse, and retention in CBR, Iiia. Csic. Es pp. 1–32, November 2016. http://www.iiia.csic.es/~mantaras/RRR_paper_KER.pdf
Hassan, B.A.R., Yusoff, Z.B.M., Othman, M.A.H., Bin, A.S.: Information is available at the end of the Chapter, https://doi.org/10.5772/55358, “We are IntechOpen , the world ’ s leading publisher of Open Access books Built by scientists , for scientists TOP 1 %,” Intech, p. 13, (2012). https://doi.org/10.1039/C7RA00172J%0A, https://www.intechopen.com/books/advanced-biometric-technologies/liveness-detection-in-biometrics%0A. https://doi.org/10.1016/j.colsurfa.2011.12.014
Policastro, C.A., Carvalho, A.C.P.L.F., Delbem, A.C.B.: Automatic knowledge learning and case adaptation with a hybrid committee approach. J. Appl. Log. 4(1), 26–38 (2006). https://doi.org/10.1016/j.jal.2004.12.002
El-Sappagh, S., Elmogy, M., Riad, A.M.: A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis. Artif. Intell. Med. 65(3), 179–208 (2015). https://doi.org/10.1016/j.artmed.2015.08.003
Pusztová, L., Babič, F., Paralič, J.: Semi-automatic adaptation of diagnostic rules in the case-based reasoning process. Appl. Sci. 11(1), 1–18 (2021). https://doi.org/10.3390/app11010292
Lee, B., Ellahi, W., Choi, J.Y.: Using deep CNN with data permutation scheme for classification of Alzheimer’s disease in structural magnetic resonance imaging (SMRI). IEICE Trans. Inf. Syst. E102D(7), 1384–1395 (2019). https://doi.org/10.1587/transinf.2018EDP7393
Chantamit-o-pas, P., Goyal, M.: A case based reasoning framework for prediction of stroke disease, no. Mi (2007)
Ikidid, A., El Fazziki, A., Sadgal, M.: A multi-agent framework for dynamic traffic management considering priority link. Int. J. Commun. Networks Inf. Secur. 13(2), 324–330 (2021). https://doi.org/10.54039/ijcnis.v13i2.4977
Ikidid, A., Abdelaziz, E.F., Sadgal, M.: Multi-agent and fuzzy inference-based framework for traffic light optimization. Int. J. Interact. Multimed. Artif. Intell. p. 1, (2021). (in Press). https://doi.org/10.9781/ijimai.2021.12.002
Ikidid, A., El Fazziki, A., Sadgal, M.: A fuzzy logic supported multi-agent system for urban traffic and priority link control. J. Univers. Comput. Sci. 27(10), 1026–1045 (2021). https://doi.org/10.3897/jucs.69750
Ikidid, A., Abdelaziz, E.F.: Multi-agent and fuzzy inference based framework for urban traffic simulation. In: Proceedings of 2019 4th International Conference on Systems of Collaboration Big Data, Internet Things Secur. SysCoBIoTS 2019, (2019). https://doi.org/10.1109/SysCoBIoTS48768.2019.9028016
Bianchi, R.A.C., López De Màntaras, R.: Case-based multiagent reinforcement learning: cases as heuristics for selection of actions. Front. Artif. Intell. Appl. 215, 355–360 (2010). https://doi.org/10.3233/978-1-60750-606-5-355
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Ichahane, M.Y., Assad, N., ouahmane, H. (2023). Case-Based Reasoning Approach, Integrating Deep Learning for Patient Diagnosis Combined X-Ray with Symptoms. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_10
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