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Syst."],"published-print":{"date-parts":[[2023,3]]},"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. <\/jats:p>","DOI":"10.1142\/s0129065723500107","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T05:26:05Z","timestamp":1671081965000},"source":"Crossref","is-referenced-by-count":13,"title":["An Evolutionary Attention-Based Network for Medical Image Classification"],"prefix":"10.1142","volume":"33","author":[{"given":"Hengde","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Shui-Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Rajeev","family":"Raman","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]},{"given":"Juan M.","family":"G\u00f3rriz","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Networking and Communications, University of Granada, Granada 52005, Spain"}]},{"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]}],"member":"219","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"S0129065723500107BIB001","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2020-0043"},{"issue":"4","key":"S0129065723500107BIB002","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/JBHI.2022.3151171","volume":"26","author":"Dhere A.","year":"2022","journal-title":"IEEE J. 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