Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
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Published:2023-08-14
Issue:16
Volume:13
Page:9226
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Yue Wenwen1, Liu Shiwei1, Li Yongming1ORCID
Affiliation:
1. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Abstract
With the development of deep learning, convolutional neural networks (CNNs) and Transformer-based methods have become key techniques for medical image classification tasks. However, many current neural network models have problems such as high complexity, a large number of parameters, and large model sizes; such models obtain higher classification accuracy at the expense of lightweight networks. Moreover, such larger-scale models pose a great challenge for practical clinical applications. Meanwhile, Transformer and multi-layer perceptron (MLP) methods have some shortcomings in terms of local modeling capability and high model complexity, and need to be used on larger datasets to show good performance. This makes it difficult to utilize these networks in clinical medicine. Based on this, we propose a lightweight and efficient pure CNN network for medical image classification (Eff-PCNet). On the one hand, we propose a multi-branch multi-scale CNN (M2C) module, which divides the feature map into four parallel branches along the channel dimensions by a certain scale factor and carries out a deep convolution operation using different scale convolution kernels, and this multi-branch multi-scale operation effectively replaces the large kernel convolution. This multi-branch multi-scale operation effectively replaces the large kernel convolution. It reduces the computational cost of the module while fusing the feature information between different channels and thus obtains richer feature information. Finally, the four feature maps are then spliced along the channel dimensions to fuse the multi-scale and multi-dimensional feature information. On the other hand, we introduce the structural reparameterization technique and propose the structural reparameterized CNN (Rep-C) module. Specifically, it utilizes multiple linear operators to generate different feature maps during the training process and fuses all the participants into one through parameter fusion to achieve fast inference while providing a more effective solution for feature reuse. A number of experimental results show that our Eff-PCNet performs better than current methods based on CNN, Transformer, and MLP in the classification of three publicly available medical image datasets. Among them, we achieve 87.4% Acc on the HAM10000 dataset, 91.06% Acc on the SkinCancer dataset, and 97.03% Acc on the Chest-Xray dataset. Meanwhile, our approach achieves a better trade-off between the number of parameters; computation; and other performance metrics as well.
Funder
Xinjiang Uygur Autonomous Region Tianshan Talent Training Program Project National Science Foundation of China Scientific and Technological Innovation 2030 major project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference56 articles.
1. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25: Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA. 2. Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20–25). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA. 3. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (May, January 29). Early diagnosis of Alzheimer’s disease with deep learning. Proceedings of the IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China. 4. E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans;Martel;Proceedings of the Medical Image Computing and Computer Assisted Intervention—MICCAI 2020—23th International Conference,2020 5. Kim, E., Kim, S., Seo, M., and Yoon, S. (2021, January 19–25). XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual.
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