Fundus Image Classification Research Based on Ensemble Convolutional Neural Network and Vision Transformer

Yuan Yuan; Minghui Chen; Shuting Ke; Teng Wang; Longxi He; Linjie Lü; Hao Sun; Jiannan Liu

doi:10.3788/CJL202249.2007205

[1] Walter T, Massin P, Erginay A et al. Automatic detection of microaneurysms in color fundus images[J]. Medical Image Analysis, 11, 555-566(2007).

[2] Zhu C Z, Xiang Y, Zou B J et al. Retinal vessel segmentation in fundus images using CART and AdaBoost[J]. Journal of Computer-Aided Design & Computer Graphics, 26, 445-451(2014).

[3] Anwar S M, Majid M, Qayyum A et al. Medical image analysis using convolutional neural networks: a review[J]. Journal of Medical Systems, 42, 226(2018).

[4] Wan C, Li H, Cao G F et al. An artificial intelligent risk classification method of high myopia based on fundus images[J]. Journal of Clinical Medicine, 10, 4488(2021).

[5] Imran A, Li J Q, Pei Y et al. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network[J]. The Visual Computer, 37, 2407-2417(2021).

[6] Lian C M, Zhong S C, Zhang T F et al. Transfer learning-based classification of optical coherence tomography retinal images[J]. Laser & Optoelectronics Progress, 58, 0117002(2021).

[7] Sun Y C, Liu Y H, Zhang D F et al. Diagnosis method of diabetic retinopathy based on deep learning[J]. Laser & Optoelectronics Progress, 57, 241701(2020).

[8] Zheng W, Shen Q H, Ren J. Recognition and classification of diabetic retinopathy based on Improved DR-Net algorithm[J]. Acta Optica Sinica, 41, 2210002(2021).

[9] Ren L J, Sun Y, Ding W P et al. Multiple lesions detection of fundus images based on CNN algorithm optimized by single population frog-leaping algorithm[J]. Journal of Frontiers of Computer Science and Technology, 15, 1762-1772(2021).

[10] Zhong Z Q, Yuan J, Tang X Y. Left-vs-right eye discrimination based on convolutional neural network[J]. Journal of Computer Research and Development, 55, 1667-1673(2018).

[11] Yu S, Ma K, Bi Q et al. MIL-VT: multiple instance learning enhanced vision transformer for fundus image classification[M]. de Bruijne M, Cattin P C, Cotin S, et al. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 129008, 45-54(2021).

[12] Tan M X, Le Q V[EB/OL]. EfficientNet: rethinking model scaling for convolutional neural networks. https://arxiv.org/abs/1905.11946

[13] Tan M X, Le Q V[EB/OL]. EfficientNetV2: smaller models and faster training. https://arxiv.org/abs/2104.00298

[14] Li T, Gao Y Q, Wang K et al. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening[J]. Information Sciences, 501, 511-522(2019).

[15] Lu H T, Zhang Q C. Applications of deep convolutional neural network in computer vision[J]. Journal of Data Acquisition & Processing, 31, 1-17(2016).

[16] Dosovitskiy A, Beyer L, Kolesnikov A et al[EB/OL]. An image is worth 16×16 words: transformers for image recognition at scale. https://arxiv.org/abs/2010.11929

[17] Yang L, Zhang R Y, Li L et al. Simam: a simple, parameter-free attention module for convolutional neural networks[C], 139, 11863-11874(2021).

[18] Xu A S, Tang L J, Chen G N. Single image rain removal method based on attention mechanism[J]. Journal of Chinese Computer Systems, 41, 1281-1285(2020).

[19] Jiang Z C, Dong Z X, Wang L Y et al. Method for diagnosis of acute lymphoblastic leukemia based on ViT-CNN ensemble model[J]. Computational Intelligence and Neuroscience, 2021, 7529893(2021).

[20] Liu W T, Lu X M. Research progress of transformer based on computer vision[J]. Computer Engineering and Applications, 58, 1-16(2022).

[21] Zhou Z H[M]. Ensemble methods: foundations and algorithms(2012).

[22] Sung W T, Chen J H, Hsiao C L. Data fusion for PT100 temperature sensing system heating control model[J]. Measurement, 52, 94-101(2014).

[23] Zhang W[D]. Research on intelligent diagnosis of fundus lesions(2021).

[24] Lindholm E, Nickolls J, Oberman S et al. NVIDIA tesla: a unified graphics and computing architecture[J]. IEEE Micro, 28, 39-55(2008).

[25] Paszke A, Gross S, Massa F et al. PyTorch: an imperative style, high-performance deep learning library[C](2019).

[26] Müller S G, Hutter F. TrivialAugment: tuning-free yet state-of-the-art data augmentation[C], 754-762(2021).

[27] Loshchilov I, Hutter F[EB/OL]. SGDR: stochastic gradient descent with warm restarts. https://arxiv.org/abs/1608.03983

[28] Loshchilov I, Hutter F[EB/OL]. Decoupled weight decay regularization. https://arxiv.org/abs/1711.05101

[29] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[30] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[31] Zhang H, Wu C R, Zhang Z Y et al[EB/OL]. ResNeSt: split-attention networks. https://arxiv.org/abs/2004.08955

[32] Han K, Xiao A, Wu E H et al[EB/OL]. Transformer in transformer. https://arxiv.org/abs/2103.00112