The diagnosis of brain tumor types generally depends on the clinical experience of doctors, and computer-assisted diagnosis improves the accuracy of diagnosing tumor types. Therefore, a convolutional neural network based on complex networks (CNNBCN) with a modified activation function for the magnetic resonance imaging classification of brain tumors is presented. The network structure is not manually designed and optimized, but is generated by randomly generated graph algorithms. These randomly generated graphs are mapped into a computable neural network by a network generator. The accuracy of the modified CNNBCN model for brain tumor classification reaches 95.49%, which is higher than several models presented by other works. In addition, the test loss of brain tumor classification of the modified CNNBCN model is lower than those of the ResNet, DenseNet and MobileNet models in the experiments. The modified CNNBCN model not only achieves satisfactory results in brain tumor image classification, but also enriches the methodology of neural network design. INDEX TERMS Convolutional neural network, complex networks, randomly generated graph, network generator, brain tumors.

h i g h l i g h t s• A formula is proposed to approximate the first-order derivative.• An optimal step length rule for the proposed formula is investigated. • A Taylor-type ZNN model is derived for time-varying matrix inversion. a b s t r a c tIn order to achieve higher computational precision in approximating the first-order derivative and discretize more effectively the continuous-time Zhang neural network (ZNN), a Taylor-type numerical differentiation rule is proposed and investigated in this paper. This rule not only greatly remedies some intrinsic weaknesses of the backward and central numerical differentiation rules, but also overcomes the limitation of the Lagrange-type numerical differentiation rules in ZNN discretization. In addition, a formula is proposed to obtain the optimal step-length of the Taylor-type numerical differentiation rule. Moreover, based on the proposed numerical differentiation rule, the stability, convergence and residual error of the Taylor-type discrete-time ZNN (DTZNN) are analyzed. Numerical experimental results further substantiate the efficacy and advantages of the proposed Taylor-type numerical differentiation rule for first-order derivative approximation and ZNN discretization.

We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help of optical flow. We validate our approach on the DAVIS Challenge and achieve rank 1 results on the DAVIS 2016 Challenge (single-object segmentation) and competitive scores on both DAVIS 2018 Semi-supervised Challenge and Interactive Challenge (multi-object segmentation). While most models tend to have increasing complexity for the challenging task of video object segmentation, FAVOS provides a simple and efficient pipeline that produces accurate predictions.

Data on stridulitral types, strial rumber, width, interspace and ontogenetic changes of stridulitral structures are provided and analyzed. Its significance on taxonomy is discussed.

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