ArticleDiagnostics (Basel, Switzerland)2023
Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.Frontiers in digital health · 2026Review
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- Cervical cancer prediction using deformable kernel darknet-53 and depth wise separable convolutional neural networks.Scientific reports · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.Scientific reports · 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
- CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.Heliyon · 2024Article
- A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer.Journal of imaging informatics in medicine · 2024Article
- Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.PloS one · 2024Article
- Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach.Cancers · 2023Article
- Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.Frontiers in oncology · 2023Review
- CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities.Digital healthArticle
- The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min-max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min-max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min-max neural network classifiers mentioned in the literature.
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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.