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An Evolutionary Attention-Based Network for Medical Image Classification.
- Source :
- International Journal of Neural Systems; Mar2023, Vol. 33 Issue 3, p1-17, 17p
- Publication Year :
- 2023
-
Abstract
- Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01290657
- Volume :
- 33
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- International Journal of Neural Systems
- Publication Type :
- Academic Journal
- Accession number :
- 162202659
- Full Text :
- https://doi.org/10.1142/S0129065723500107