ArticleScientific reports2025
An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline 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
- Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.Scientific reports · 2026Article
- Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creation of tumors within the ovaries. The high death rate and often delayed discovery of Ovarian Cancer make it a serious healthcare concern. Due to the annual 207,000 fatalities and 314,000 new cases worldwide, Ovarian Cancer poses a serious threat to public health, making quick and precise detection and classification techniques more essential. This work discusses the importance of Ovarian Cancer diagnosis and presents a new model for Ovarian Cancer classification. It also showcases a comparative analysis with other state-of-the-art models for Ovarian Cancer. Using an Ovarian Cancer image dataset which has data samples named Clear Cell, Endometri, Mucinous, Serous, and Non-Cancerous, it compares the proposed OvCan-FIND model to a wide range of CNN-based architectures, such as Inception V3, different EfficientNet variants, ResNet152V2, MobileNet, MobileNetV2, VGG16, VGG19, and Xception. The study examines the most recent Ovarian Cancer classification algorithms in this context to increase prognosis and diagnostic accuracy; our proposed OvCan-FIND model outperforms base models with an exceptional accuracy of 99.74%. This model presents significant prospects for enhancing ovarian cancer early identification and diagnosis, which will ultimately enhance patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
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.