ArticleBioscience reports2018
Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.
Article in Bioscience reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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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
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.Cell reports. Medicine · 2026Article
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Review
- Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer.BMC medical imaging · 2025Article
- OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis.Scientific reports · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Role of artificial intelligence in digital pathology for gynecological cancers.Computational and structural biotechnology journal · 2024Review
- Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.BJS open · 2024Article
- Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.Bioengineering (Basel, Switzerland) · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.Scientific reports · 2023Article
- Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.Cancers · 2023Article
- Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.The Analyst · 2023Article
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning Approaches in Histopathology.Cancers · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.
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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.