An Evolutionary Attention-Based Network for Medical Image Classification

Hengde Zhu, Jian Wang, Shui Hua Wang, Rajeev Raman, Juan M. Górriz, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Article number2350010
JournalInternational Journal of Neural Systems
Issue number3
Publication statusPublished - 1 Mar 2023
Externally publishedYes


  • Evolutionary networks
  • attention mechanism
  • deep learning
  • medical image analysis


Dive into the research topics of 'An Evolutionary Attention-Based Network for Medical Image Classification'. Together they form a unique fingerprint.

Cite this