DMINet: A lightweight dual-mixed channel-independent network for cataract recognition

Xiao Wu; Yu Chen; Qiuyang Yan; Yuhang Zhao; Jilu Zhao; Xiaoqing Zhang; Risa Higashita; Jiang Liu

10.1109/IJCNN54540.2023.10191292

2023

International Joint Conference on Neural Networks (IJCNN)

2161-4393

978-1-6654-8868-6

2023 International Joint Conference on Neural Networks (IJCNN)

2023-June

1-8

18-23 June 2023

Gold Coast, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Cataracts are the leading cause of visual impairment and blindness globally, attracting abroad attention from society. Over the years, researchers have developed many state-of-the-art convolutional neural networks (CNNs) to recognize cataract severity levels based on different ophthalmic images. However, most current works focus on improving cataract recognition performance by designing complex CNNs, often ignoring resource-constrained medical device limitations. To this problem, this paper proposes a novel dual-mixed channel-independent convolution (DMIConv) method, which takes advantage of the multiscale convolution kernels by combining a depthwise convolution with a depthwise dilated convolution sequentially. Moreover, we build a lightweight dual-mixed channel-independent network (DMINet) to recognize cataracts. To verify the effectiveness and efficiency of DMINet, we conduct extensive experiments on a clinical anterior segment optical coherence tomography (AS-OCT) dataset of nuclear cataract (NC) and a publicly available OCT dataset. The results show that our proposed DMINet keeps a better tradeoff between the model complexity and the classification performance than efficient CNNs, e.g., DMINet outperforms MixNet by 3.34% of accuracy by using 4.58% fewer parameters.

cataract DMIConv classification convolution lightweight

第一

英语

CPCI-S ; EI

National Natural Science Foundation of China[82272086]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic

WOS:001046198702002

20233614678500

Complex networks ; Convolutional neural networks ; Medical imaging ; Optical tomography

Biomedical Engineering:461.1 ; Information Theory and Signal Processing:716.1 ; Computer Systems and Equipment:722 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746

IEEE

Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Xiao Wu,Yu Chen,Qiuyang Yan,et al. DMINet: A lightweight dual-mixed channel-independent network for cataract recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-8.