ArticleBioMed research international2023
HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.
Article in BioMed research international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.BMC cancer · 2026Pooled 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
- A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.Scientific reports · 2026Article
- Optimized KNN with domain-informed features and LIME explainability for improved breast cancer classification.BMC medical informatics and decision making · 2026Article
- Joint segmentation and classification of cervical cells using U-Net and CNN ensemble for early detection of cervical cancer.Cancer cell international · 2026Article
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- MGWO-CNN: hyperparameter optimization of CNN classifier for cervical cancer detection using Modified Grey Wolf Optimizer.Scientific reports · 2025Article
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- Advancements in fusion-based deep representation learning for enhanced cervical precancerous lesion classification using biomedical image analysis.Scientific reports · 2025Article
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- Multi-Modal Graph Neural Networks for Colposcopy Data Classification and Visualization.Cancers · 2025Article
- A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides.Scientific reports · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.Scientific reports · 2025Article
- CERVIXNET: An Efficient Approach for the Detection and Classifications of the Cervigram Images Using Modified Deep Learning Architecture.Current medical imaging · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images.Frontiers in oncology · 2024Article
- Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.