ArticleDiagnostics (Basel, Switzerland)2023
Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks 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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 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
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.Discover oncology · 2025Article
- Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- A related convolutional neural network for cancer diagnosis using microRNA data classification.Healthcare technology letters · 2024Article
- Content-Based Image Retrieval and Image Classification System for Early Prediction of Bladder Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Convolutional MLP orthogonal fusion of multiscale features for visual place recognition.Scientific reports · 2024Article
- Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.Scientific reports · 2024Article
- A Multilayer Perceptron-Based Spherical Visual Compass Using Global Features.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.Scientific reports · 2024Article
- High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt.Bioengineering (Basel, Switzerland) · 2023Article
- Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- Efficient and Automatic Breast Cancer Early Diagnosis System Based on the Hierarchical Extreme Learning Machine.Sensors (Basel, Switzerland) · 2023Article
- A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound 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
- Fire Detection in Ship Engine Rooms Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images.Technology in cancer research & treatmentArticle
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.
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