Abstract
Medical diagnosis from images supports clinicians in their profession. In practical dentistry, diseases are found mainly on experience of dentists regarding dental structures and explicit symptoms of patients. In this paper, in order to reduce errors in medical diagnosis problem from images, we introduce a new diagnostic model based on intuitionistic fuzzy distance measures with parameter learning. A new intuitionistic fuzzy distance measure named Modified H-max is proposed to calculate similarity degree between an input image and all patterns of corresponding disease patterns. Parameters of the proposed measure are trained to optimize performance. Hence, the new diagnosis model has the advantages of using the cross-evaluation degree of H-max measure and weight optimization. The proposed algorithm is experimentally validated on real datasets of Hanoi Medical University, Vietnam against related methods.
This research is supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2017.02. The author (R. T. Ngan) would like to thank the Project 911 of VNU University of Science, Vietnam National University for supporting her work.
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The link https://source-forge.net/projects/DIMHM/ provides the code and dataset of this paper.
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Ngan, R.T., Cuong, B.C., Tuan, T.M., Son, L.H. (2018). Medical Diagnosis from Images with Intuitionistic Fuzzy Distance Measures. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_37
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