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Abstract

Medical image recognition is affected by characteristics such as blur and noise, which cause medical image features that cannot be effectively identified and directly affects clinical diagnostics. In order to improve the diagnostic effect of medical MR image features, based on the FRFCM clustering segmentation method, this study combines the medical MR image feature reality, collects data for traditional clustering method analysis, and sorts out the shortcomings of traditional clustering methods. Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.

Keywords

FRFCM clustering feature extraction image segmentation medical diagnosis

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References

, Zang

, Chai

, et al., Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models[J], Medical Physics 43(10) (2016), 5426–5436.

, Chen

, Shi

, et al., Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images[J], IEEE transactions on Image Processing 24(12) (2015), 5315–5329.

Song

, Kang

, Zhang

, et al., Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm[J], BMC Systems Biology 9 Suppl 5(Suppl 5) (2015), S5.

Yang-Yuan

, Cheng-Tang

, Chen-Ming

, et al., Enteroscopic Diagnosis and Management of Small Bowel Diverticular Hemorrhage: A Multicenter Report from the Taiwan Association for the Study of Small Intestinal Diseases[J], Research and Practice 2015 (2015), 1–4.

Zhou

, Ke

and Luo

, Multi-camera transfer GAN for person re-identification, J Vis Commun Image Represent 59 (2019), 393–400.

Lokesh

, Kanisha

, Nalini

, et al., Speech to speech interaction system using Multimedia Tools and Partially Observable Markov Decision Process for visually impaired students[J], Multimedia Tools and Applications 2018.

Roberto

and Toppano

, Multimedia analysis and design: a conceptual framework[J], Multimedia Tools and Applications 2019(1/4).

, Zhao

, Chen

, et al., Performance and power consumption tradeoff in multimedia cloud[J], Multimedia Tools and Applications 2018.

Bakiya

, Kamalanand

, Rajinikanth

, et al., Deep neural network assisted diagnosis of time-frequency transformed electromyograms[J], Multimedia Tools and Applications 2018.

10.

Zhou

and Tan

, Electrocardiogram soft computing using hybrid deep learning CNN-ELM, Soft Comput 86 (2020).

11.

Yen

V.T.

, Nan

W.Y.

and Van Cuong

, Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators, Neural Comput & Applic 31 (2019), 6945–6958.

12.

Urtnasan

, Park

and Lee

, Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal, Neural Comput & Applic 32 (2020), 4733–4742.

13.

Kaur

and Singh

, Hybrid intrusion detection and signature generation using Deep Recurrent Neural Networks, Neural Comput & Applic 32 (2020), 7859–7877.

14.

Sakar

C.O.

, Polat

S.O.

, Katircioglu

, et al., Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks, Neural Comput & Applic 31 (2019), 6893–6908.

15.

Chaira

, Intuitionistic fuzzy color clustering of human cell images on different color models[J], Journal of Intelligent & Fuzzy Systems 23(2, 3) (2012), 43–51.

16.

Chaira

, Medical image processing: Advanced fuzzy set theoretic techniques[M], CRC Press, 2015.

17.

Iakovidis

D.K.

, Georgakopoulos

S.V.

, Vasilakakis

, et al., Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification[J], IEEE transactions on medical imaging 37(10) (2018), 2196–2210.

18.

Ananthi

V.P.

, Balasubramaniam

and Kalaiselvi

, A new fuzzy clustering algorithm for the segmentation of brain tumor[J], Soft Computing 20(12) (2016), 4859–4879.

19.

Verma

and Merigó

J.M.

, On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision-making[J], International Journal of Intelligent Systems 34(10) (2019), 2556–2583.

20.

Tripathy

B.K.

, Sooraj

T.R.

and Mohanty

R.K.

, A new approach to interval-valued fuzzy soft sets and its application in decision-making[M], Advances in Computational Intelligence. Springer, Singapore, 2017:3–10.

21.

Chaudhuri

and De

, Fuzzy support vector machine for bankruptcy prediction[J], Applied Soft Computing 11(2) (2011), 2472–2486.

FRFCM clustering segmentation method for medical MR image feature diagnosis

Abstract

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