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The automated classification of breast cancer types based on histopathological images is a challenging endeavor, wherein computer-assisted diagnosis serves as a reference for pathologists\u2019 decision-making. Addressing the automated breast cancer classification task, this paper proposes a novel DenLsNet neural network model, featuring a combined DenseNet\u2212LSTM architecture for efficient feature extraction and classification. First, the feature extraction process is optimized by incorporating squeeze-and-excitation (SE) blocks into a pretrained improved dense convolutional network (DenseNet). Next, iterative convolutional feature fusion (iCFF) blocks are introduced for deep and shallow feature fusion. To enhance the classification performance, the original DenseNet classifier is replaced with a specially designed long short-term memory (LSTM)-based classifier, which proves effective in capturing long-distance relationships in image sequences, improving the model\u2019s sensitivity to breast cancer variations. Performance evaluation experiments, conducted on the BreakHis and BACH public datasets, demonstrate significant performance enhancement in the multi-class classification task, with DenLsNet exhibiting superior performance compared to state-of-the-art models. Additionally, the proposed model achieves commendable results in the binary classification task, indicating strong generalization capabilities.<\/jats:p>","DOI":"10.1007\/s11227-025-07383-8","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T16:00:14Z","timestamp":1748620814000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DenLsNet-C: a novel model for breast cancer classification in pathology images based on DenseNet and LSTM"],"prefix":"10.1007","volume":"81","author":[{"given":"Yihan","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengnan","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunling","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanlin","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Ganchev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"7383_CR1","unstructured":"Wild CP, Weiderpass E, Stewart BW (2020) World cancer report"},{"key":"7383_CR2","doi-asserted-by":"crossref","unstructured":"Siegel RL, Miller KD, Jemal A.J.C (2018) Cancer statistics 2018, 68:7\u201330.","DOI":"10.3322\/caac.21442"},{"key":"7383_CR3","doi-asserted-by":"publisher","first-page":"5023","DOI":"10.1007\/s11831-023-09968-z","volume":"30","author":"B Abhisheka","year":"2023","unstructured":"Abhisheka B, Biswas SK, Purkayastha B (2023) A comprehensive review on breast cancer detection, classification and segmentation using deep learning. 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