Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder
Open accessAbstract
Discrimination of ovarian tumors is necessary for proper treatment. In this study, we developed a convolutional neural network model with a convolutional autoencoder (CNN-CAE) to classify ovarian tumors. A total of 1613 ultrasound images of ovaries with known pathological diagnoses were pre-processed and augmented for deep learning analysis. We designed a CNN-CAE model that removes the unnecessary information (e.g., calipers and annotations) from ultrasound images and classifies ovaries into five classes. We used fivefold cross-validation to evaluate the performance of the CNN-CAE model in terms of accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) was applied to visualize and verify the CNN-CAE model results qualitatively. In classifying normal versus ovarian tumors, the CNN-CAE model showed 97.2% accuracy, 97.2% sensitivity, and 0.9936 AUC with DenseNet121 CNN architecture. In distinguishing malignant ovarian tumors, the CNN-CAE model showed 90.12% accuracy, 86.67% sensitivity, and 0.9406 AUC with DenseNet161 CNN architecture. Grad-CAM showed that the CNN-CAE model recognizes valid texture and morphology features from the ultrasound images and classifies ovarian tumors from these features. CNN-CAE is a feasible diagnostic tool that is capable of robustly classifying ovarian tumors by eliminating marks on ultrasound images. CNN-CAE demonstrates an important application value in clinical conditions.
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Poor image quality was a frequent limitation.
“poor image quality [186,190]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [190]
The Discussion (page unavailable) reports that calipers and annotations on ultrasound images can affect the model’s results, supporting a specific image-quality concern. It does not describe poor image quality as a frequent limitation; its stated study limitations are the small retrospective sample and use of still images. (LLM, conf=0.78)
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Cited by
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“…homogeneity of data due to a single examiner or single center study [188,192], a single investigated ethnicity [189], absent external validation [192,193], poor image quality [186,190], and, most importantly, small sample sizes not sufficient enough to train the algorithm [184,185,187,188,192,193].”
Background
- Authors
- Jung, Y., Kim, T., Han, M.-R., Kim, S., Kim, G., Lee, S., & Choi, Y. J.
- Affiliations
- Soonchunhyang University Hospital Bucheon (KR)
- Pohang University of Science and Technology (KR)
- Incheon National University (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Corresponding author
- Choi, Youn Jin
- Venue
- Scientific Reports
- Published
- 2022-10-11
- Volume
- 12
- Issue
- 1
- Pages
- 17024-17024
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9554195
- OpenAlex
- W4304193965
- Cited by
- 67
- References
- 17
- Citations by year
- 2026: 9 · 2025: 26 · 2024: 22 · 2023: 10
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-01-25)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the National Research Foundation of Korea (NRF) grant funded by the Korea Government (2020R1A2C1009744)
- the Priority Research Centers Program through the National Research Foundation of Korea (2020R1A6A1A03047902, 2018M3A9E8021512)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- ovarian tumor classification, ovarian ultrasonography, convolutional autoencoder, image denoising, ovarian cancer diagnosis, Grad-CAM, DenseNet, image augmentation
- MeSH terms
- Female, Humans, Ovarian Neoplasms, ROC Curve, Neural Networks, Computer
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