Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Jafar Tavoosi; Chunwei Zhang; Ardashir Mohammadzadeh; Saleh Mobayen; Amir H Mosavi

doi:10.3389/fninf.2021.667375

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Front Neuroinform. 2021 Sep 1:15:667375. doi: 10.3389/fninf.2021.667375. eCollection 2021.

Authors

Jafar Tavoosi¹, Chunwei Zhang², Ardashir Mohammadzadeh³, Saleh Mobayen⁴, Amir H Mosavi^{5

6}

Affiliations

¹ Department of Electrical Engineering, Ilam University, Ilam, Iran.
² Structural Vibration Control Group, Qingdao University of Technology, Qingdao, China.
³ Department of Electrical Engineering, University of Bonab, Bonab, Iran.
⁴ Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan.
⁵ Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany.
⁶ Institute of Software Design and Development, Obuda University, Budapest, Hungary.

Abstract

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

Keywords: 2D to 3D; artificial intelligence; brain MRI; image interpolation; machine learning; recurrent neural network; type-2 fuzzy system.