ArticleSensors (Basel, Switzerland)2022
Hybrid Loss-Constrained Lightweight Convolutional Neural Networks for Cervical Cell Classification.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 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
- Multi-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification.PloS one · 2026Article
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.Medical & biological engineering & computing · 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
- Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.Frontiers in oncology · 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
- Improvement of Electromagnetic Side-Channel Information Measurement Platform.Sensors (Basel, Switzerland) · 2023Article
- Pap Smear Images Classification Using Machine Learning: A Literature Matrix.Diagnostics (Basel, Switzerland) · 2022Review
- Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures.Sensors (Basel, Switzerland) · 2022Article
- 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.
Funding
Abstract
Artificial intelligence (AI) technologies have resulted in remarkable achievements and conferred massive benefits to computer-aided systems in medical imaging. However, the worldwide usage of AI-based automation-assisted cervical cancer screening systems is hindered by computational cost and resource limitations. Thus, a highly economical and efficient model with enhanced classification ability is much more desirable. This paper proposes a hybrid loss function with label smoothing to improve the distinguishing power of lightweight convolutional neural networks (CNNs) for cervical cell classification. The results strengthen our confidence in hybrid loss-constrained lightweight CNNs, which can achieve satisfactory accuracy with much lower computational cost for the SIPakMeD dataset. In particular, ShufflenetV2 obtained a comparable classification result (96.18% in accuracy, 96.30% in precision, 96.23% in recall, and 99.08% in specificity) with only one-seventh of the memory usage, one-sixth of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). GhostNet achieved an improved classification result (96.39% accuracy, 96.42% precision, 96.39% recall, and 99.09% specificity) with one-half of the memory usage, one-quarter of the number of parameters, and one-fiftieth of total flops compared with Densenet-121 (96.79% in accuracy). The proposed lightweight CNNs are likely to lead to an easily-applicable and cost-efficient automation-assisted system for cervical cancer diagnosis and prevention.
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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.