Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

• Published in: Frontiers in neuroinformatics Vol. 15; p. 667375
• Main Authors: Tavoosi, Jafar, Zhang, Chunwei, Mohammadzadeh, Ardashir, Mobayen, Saleh, Mosavi, Amir H.
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 01.09.2021; Frontiers Media S.A
• Subjects: 2D to 3D; Accuracy; Algorithms; artificial intelligence; brain MRI; Computer graphics; Deep learning; Diagnosis; Fuzzy logic; image interpolation; Image processing; Interpolation; Medical imaging; Neural networks; Neuroscience; recurrent neural network; Teaching methods; type-2 fuzzy system
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2021.667375

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.