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Computer-aided diagnosis of cervical dysplasia using colposcopic images
- Source :
- Frontiers in Oncology, Vol 12 (2022)
- Publication Year :
- 2022
- Publisher :
- Frontiers Media S.A., 2022.
-
Abstract
- Backgroundcomputer-aided diagnosis of medical images is becoming more significant in intelligent medicine. Colposcopy-guided biopsy with pathological diagnosis is the gold standard in diagnosing CIN and invasive cervical cancer. However, it struggles with its low sensitivity in differentiating cancer/HSIL from LSIL/normal, particularly in areas with a lack of skilled colposcopists and access to adequate medical resources.Methodsthe model used the auto-segmented colposcopic images to extract color and texture features using the T-test method. It then augmented minority data using the SMOTE method to balance the skewed class distribution. Finally, it used an RBF-SVM to generate a preliminary output. The results, integrating the TCT, HPV tests, and age, were combined into a naïve Bayes classifier for cervical lesion diagnosis.Resultsthe multimodal machine learning model achieved physician-level performance (sensitivity: 51.2%, specificity: 86.9%, accuracy: 81.8%), and it could be interpreted by feature extraction and visualization. With the aid of the model, colposcopists improved the sensitivity from 53.7% to 70.7% with an acceptable specificity of 81.1% and accuracy of 79.6%.Conclusionusing a computer-aided diagnosis system, physicians could identify cancer/HSIL with greater sensitivity, which guided biopsy to take timely treatment.
Details
- Language :
- English
- ISSN :
- 2234943X
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Oncology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.05f19eed7ea3433088f8eed71bc58085
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fonc.2022.905623