ArticleScientific reports2025
An automatic cervical cell classification model based on improved DenseNet121.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Characteristic wavelength selection for rice blast based on hyperspectral remote sensing and deep convolutional neural networks.Pest management science · 2026Article
- An Attention-Residual Hybrid CNN for CT-Based Multiclass Classification of Alcohol-Related Liver Disease: Differential Diagnosis Against HBV-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2026Article
- RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.Scientific reports · 2026Article
- Joint segmentation and classification of cervical cells using U-Net and CNN ensemble for early detection of cervical cancer.Cancer cell international · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion.Scientific reports · 2025Article
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
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Authors and funding
3 authors.
Funding
Abstract
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model's focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.
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