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Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer
Peng Guo1, Zhiyun Xue1, Zac Mtema2
1Communications Engineering Branch, Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
Diagnostics (Basel, Switzerland)
|July 9, 2020
Summary
A new deep learning algorithm accurately identifies cervix images for precancer screening, improving accuracy in low-resource settings. This automated visual examination (AVE) method enhances cervical cancer detection by filtering inadequate images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Automated Visual Examination (AVE) is a deep learning algorithm designed to enhance cervical precancer screening effectiveness, especially in resource-limited regions.
- Accurate identification of the cervix in digital images is crucial for reliable screening, but image quality and content can be variable.
- Previous efforts focused on image quality, but a method to ensure the cervix is sufficiently present was needed.
Purpose of the Study:
- To develop and evaluate a novel algorithm for identifying images containing the cervix to a sufficient extent for automated cervical precancer screening.
- To differentiate between cervix images and non-cervix or inadequate images, reducing manual labor and potential errors.
- To improve the robustness of deep learning-based cervical screening tools by ensuring image relevance.
Main Methods:
- An ensemble deep learning method was developed, combining three architectures: RetinaNet (object detection), Deep SVDD (one-class classification), and a customized Convolutional Neural Network (CNN) (binary classification).
- The ensemble model was trained and tested on a large dataset of over 30,000 smartphone-acquired cervical images.
- Performance was evaluated by comparing the ensemble's accuracy against individual model performances.
Main Results:
- The ensemble deep learning method achieved an average accuracy of 91.6% and an F-1 score of 0.890 on the test dataset.
- Individual architectures also showed promising results, but the ensemble approach demonstrated superior performance in identifying relevant cervix images.
- The algorithm effectively distinguished between images containing the cervix and those that were inadequate or showed other subjects.
Conclusions:
- The novel ensemble deep learning algorithm reliably identifies images containing the cervix, a critical step for accurate automated visual examination in cervical precancer screening.
- This method significantly reduces the need for manual image review, making large-scale screening more efficient and cost-effective, particularly in low- and medium-resource settings.
- The developed algorithm enhances the reliability of AI-driven cervical screening by ensuring the quality and relevance of input images.
