[1] Bidkar P S, Kumar R, Ghosh A. SegNet and salp water optimization-driven deep belief network for segmentation and classification of brain tumor[J]. Gene Expression Patterns, 45, 119248(2022).

[2] Xie Y T, Zaccagna F, Rundo L et al. Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): review, challenges, and future perspectives[J]. Diagnostics, 12, 1850(2022).

[3] Alanazi M F, Ali M U, Hussain S J et al. Brain tumor/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model[J]. Sensors, 22, 372(2022).

[4] Cui B, Jia Z N, Ji P et al. Brain tumor classification method based on improved EfficientNetV2 network[J]. Journal of Jilin University (Science Edition), 61, 1169-1177(2023).

[5] Sit M, Seo B C, Demiray B et al. EfficientRainNet: leveraging EfficientNetV2 for memory-efficient rainfall nowcasting[J]. Environmental Modelling & Software, 176, 106001(2024).

[6] Zhang X L, Wang Y, Tang Y H et al. Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification[J]. Health Information Science and Systems, 12, 11(2024).

[7] Rahim Khan W, Mustafa Madni T, Iqbal Janjua U et al. A hybrid attention-based residual unet for semantic segmentation of brain tumor[J]. Computers, 76, 647-664(2023).

[8] Rastogi D, Johri P, Tiwari V et al. Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross validation deep learning framework[J]. Biomedical Signal Processing and Control, 88, 105602(2024).

[9] Dikande Simo A M, Tchagna Kouanou A, Monthe V et al. Introducing a deep learning method for brain tumor classification using MRI data towards better performance[J]. Informatics in Medicine Unlocked, 44, 101423(2024).

[10] Rajeev S K, Pallikonda Rajasekaran M, Vishnuvarthanan G et al. A biologically-inspired hybrid deep learning approach for brain tumor classification from magnetic resonance imaging using improved Gabor wavelet transform and Elmann-BiLSTM network[J]. Biomedical Signal Processing and Control, 78, 103949(2022).

[11] Xia J M, Xing L P, Tan L et al. Brain tumors classification based on MDM-ResNet[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 14, 212-219(2022).

[12] Dutta T K, Nayak D R, Zhang Y D. ARM-Net: attention-guided residual multiscale CNN for multiclass brain tumor classification using MR images[J]. Biomedical Signal Processing and Control, 87, 105421(2024).

[13] Huang M, Xiong Z Y, Zhu J L. MRI brain tumor classification based on multi-scale residual network[J]. Chinese Journal of Magnetic Resonance Imaging, 14, 124-129(2023).

[14] Jiang L Q, Ning C Y, Yu H T. Classification model based on fusion of multi-scale feature and channel feature for benign and malignant brain tumors[J]. Chinese Optics, 15, 1339-1349(2022).

[15] Hao W Y, Cai H Y, Zuo T T et al. Intravascular ultrasound image segmentation fusing transformer branch and topology enforcement[J]. Laser & Optoelectronics Progress, 61, 1237008(2024).

[16] Asiri A A, Shaf A, Ali T et al. Advancing brain tumor detection: harnessing the Swin Transformer’s power for accurate classification and performance analysis[J]. PeerJ Computer Science, 10, e1867(2024).

[17] Şahin E, Özdemir D, Temurtaş H. Multi-objective optimization of ViT architecture for efficient brain tumor classification[J]. Biomedical Signal Processing and Control, 91, 105938(2024).

[18] Zhang Z G, Yu P F, Li H Y et al. Fine-grained image recognition of wild mushroom based on multiscale feature guide[J]. Laser & Optoelectronics Progress, 59, 1210016(2022).

[19] Qu Y L, Wang Y, Zhang Q et al. Point cloud analysis method based on spatial feature attention mechanism[J]. Laser & Optoelectronics Progress, 60, 2415003(2023).

[20] Wang D Z, Li F, Yan C Y et al. Lightweight apple-leaf pathological recognition based on multiscale fusion[J]. Laser & Optoelectronics Progress, 60, 0210005(2023).

[21] Mao Z Y, Liu Y H, Yang C Y et al. A lightweight detection method for tea disease by merging attention and multiple-knowledge-transfer[J]. Journal of Chinese Agricultural Mechanization, 45, 140-147, 2(2024).

[22] Li X X, Chen J H, Wu R et al. A method for identifying and classifying pepper diseases based on improved MaxViT[J]. Journal of Huazhong Agricultural University, 43, 123-133(2024).

[23] Chen Y, Qiao X, Qin F et al. IPMCNet: a lightweight algorithm for invasive plant multiclassification[J]. Agronomy, 14, 333(2024).

[24] Latif G, Ben Brahim G, Iskandar D N F A et al. Glioma tumors’ classification using deep-neural-network-based features with SVM classifier[J]. Diagnostics, 12, 1018(2022).

[25] Murthy M Y B, Koteswararao A, Babu M S. Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis[J]. Biomedical Engineering Letters, 12, 37-58(2021).

[26] Li X R, Zhang Y F, Kang X D et al. Intelligent diagnosis of brain tumor with MRI based on ensemble learning[J]. Computer Science, 51, 380-386(2024).

[27] Sandhiya B, Raja S K S. Deep learning and optimized learning machine for brain tumor classification[J]. Biomedical Signal Processing and Control, 89, 105778(2024).