Aiming at the complexity of the glioma classification process, a classification framework based on SE-ResNeXt network is proposed to simplify the classification process of benign and malignant of gliomas. In addition, three optimization strategies are adopted to improve the accuracy. Firstly, the MultiStepLR strategy is used to adjust the learning rate dynamically in order to improve the learning ability of the network. Secondly, the one-hot label is optimized by the label smoothing strategy which can reduce the dependence of the network on the probability distribution of real labels and improve the prediction ability of the network. Finally, the transfer learning process is simplified by the transfer learning strategy on CE-MRI dataset, and the generalization ability of the network is improved. The experimental results show that the accuracy, sensitivity, specificity and AUC of the proposed method reach 97.45%, 98.35%, 94.93% and 0.9966 for the BraTS2017 dataset, 98.99%, 99.18%,98.33% and 0.9993 for the BraTS2019 dataset, respectively. Compared with the classical networks and other algorithms, the classification framework proposed in this paper has the best performance on glioma classification.

Aiming at the problems of low contrast and blurred edge textures in medical image fusion, a new fusion scheme in non‐subsampled contourlet transform (NSCT) domain is proposed to improve the quality of fused brain images which is based on pulse‐coupled neural network (PCNN) and shuffled frog‐leaping algorithm (SFLA). First, the source images are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSCT; if one of the source images is multicolour, then hue, saturation and brightness (HSI) transform is needed first. Second, different PCNN fusion rules are designed for LF and HF subbands according to their features, respectively. Parameters including decay time constants and amplification factors are optimised by SFLA. Finally, the fused image is reconstructed by inverse NSCT; and if necessary, an inverse HSI transform is needed. Visual and quantitative analysis of experimental results show that the fused image preserves more information of the source images, and the ability of edge retention is strong. The scheme has prominent advantages in mutual information and QAB/F for multimodal brain images, including MRI‐PET, MRI‐SPECT, and CT‐MRI, which proves that it can obtain better visual effect and have strong robustness as well as wide applications.