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.

中文翻译：

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使用循环 2 型模糊神经网络的医学图像插值。

图像插值是图像处理和计算机图形学在医学成像的广泛应用中必不可少的过程。对于医学诊断中使用的图像插值，二维 (2D) 到三维 (3D) 转换可以显着减少人为错误，从而做出更好的决策。本研究提出了 2 类模糊神经网络方法，该方法是模糊逻辑和神经网络以及循环 2 类模糊神经网络 (RT2FNN) 的混合体，用于推进新的 2D 到 3D 策略。研究了所提出的方法在逼近图像插值函数方面的能力。结果表明，这两种提出的方​​法对于医学诊断都是可靠的。然而，RT2FNN 模型优于类型 2 模糊神经网络模型。循环网络和典型网络的平均平方误差分别为 0.016 和 0.025。另一方面，循环网络和典型网络的模糊规则数量分别为 16 和 22。