ArticleBioengineering (Basel, Switzerland)2022
Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.Scientific reports · 2026Article
- Light-XAI: a CADx for explainable cervical cancer detection via attention-based lightweight convolutional neural networks and layer-wise feature fusion.BioData mining · 2026Article
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- Multi-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification.PloS one · 2026Article
- Adaptive quadtree-based segmentation of nucleus and cytoplasm in pap-smear images: a lightweight and interpretable approach for automated cytology.Frontiers in artificial intelligence · 2026Article
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- Editorial for the Special Issue "Advances in Medical Image Processing, Segmentation, and Classification".Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Optimizing cervical cancer diagnosis with accurate cell classification using modified HDFF.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Deep dive into deep learning methods for cervical cancer detection and classification.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Review
- Detection of Cervical Lesion Cell/Clumps Based on Adaptive Feature Extraction.Bioengineering (Basel, Switzerland) · 2024Article
- Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.Interdisciplinary sciences, computational life sciences · 2024Article
- A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer.Journal of imaging informatics in medicine · 2024Article
- A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images.Bioengineering (Basel, Switzerland) · 2023Article
- ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Advanced Time-Frequency Methods for ECG Waves Recognition.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.
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What OpenQuestion holds
Registered trials
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.