ArticleJournal of cancer research and clinical oncology2024
Multi-classification of breast cancer pathology images based on a two-stage hybrid network.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Reciprocal cooperative gating fusion of SqueezeNet and ShuffleNetV2 for breast cancer detection in histopathology images.Scientific reports · 2026Article
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
BACKGROUND AND
objectiveIn current clinical medicine, pathological image diagnosis is the gold standard for cancer diagnosis. After pathologists determine whether breast lesions are malignant or benign, further sub-type classification is often necessary.
methodsFor this task, this study designed a multi-classification model for breast cancer pathological images based on a two-stage hybrid network. Due to limited sample size for breast sub-type data, this study selected the ResNet34 network as the base network and improved it as the first-level convolutional network, using transfer learning to assist network training. In order to compensate for the lack of long-distance dependencies in the convolutional network, the second-level network was designed to use Long Short-Term Memory (LSTM) to capture contextual information in the images for predictive classification.
resultsFor the 8 sub-types of breast cancer classification on the BreakHis (40×, 100×, 200×, 400×) dataset, the ensemble model achieved accuracy rates of 93.67%, 97.08%, 98.01%, and 94.73% respectively. For the 4 sub-types of breast cancer classification on the ICIAR2018 (200×) dataset, the ensemble model achieved accuracy, precision, recall, and F1 Score rates of 93.75%, 92.5%, 92.5%, and 92.5% respectively.
conclusionThe results show that the multi-classification model proposed in this study outperforms other methods in terms of classification performance, and further demonstrate that the proposed RFSAM module is beneficial for improving model performance.
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