ArticleHeliyon2024
CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Enhanced lung cancer detection using BiLSTM attention and transformer models on clinical text data.Scientific reports · 2026Article
- Multi-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification.PloS one · 2026Article
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- Offline reinforcement learning combining generalized advantage estimation and modality decomposition interaction.Scientific reports · 2025Article
- Pixel level deep reinforcement learning for accurate and robust medical image segmentation.Scientific reports · 2025Article
- Boosting adversarial transferability in vision-language models via multimodal feature heterogeneity.Scientific reports · 2025Article
- Force Map-Enhanced Segmentation of a Lightweight Model for the Early Detection of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Attention-enhanced deep learning for cervical cytology: combining convolutional networks with multi-head attention and fuzzy logic.Polish journal of radiology · 2025Article
- Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed TomographyCurrent medical imaging · 2025Article
- CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention.Scientific reports · 2024Article
- Article
- A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.Scientific reports · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy. Unlike the latest state-of-the-art classification method, we create a new architecture called the Reinforcement Learning Cancer Network, "RL-CancerNet", which diagnoses cervical cancer with incredible accuracy. We trained and tested our method on two well-known publicly available datasets, SipaKMeD and Herlev, to assess it and enable comparisons with earlier methods. Cervical cancer images were labeled in this dataset; therefore, they had to be marked manually. Our study shows that, compared to previous approaches for the assignment of classifying cervical cancer as an early cellular change, the proposed approach generates a more reliable and stable image derived from images of datasets of vastly different sizes, indicating that it will be effective for other datasets.
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