A segmentation model to detect cevical lesions based on machine learning of colposcopic images
Social sciences (General)
H1-99
Artificial intelligence
Q1-390
Segmentation
Science (General)
HSIL
Colposcopy
Diagnosis
3. Good health
Research Article
DOI:
10.1016/j.heliyon.2023.e21043
Publication Date:
2023-10-20T07:20:32Z
AUTHORS (20)
ABSTRACT
Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel classification and object detection but fall short tasks. Enhancing the accuracy efficiency of detecting high-level cervical lesions invasive cancer poses a primary challenge model development.Between 2018 2022, we retrospectively studied total 777 patients, comprising 339 patients with 313 microinvasive or cancer. Overall, 1554 colposcopic images were put into DeepLabv3+ for learning. Accuracy, Precision, Specificity, mIoU employed to evaluate performance prediction cancer.Experiments showed that our had better diagnosis than experts other artificial intelligence models, reached Accuracy 93.29 %, Precision 87.2 Specificity 90.1 80.27 respectively.The good post-acetic-acid can assist colposcopists improving
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (33)
CITATIONS (9)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....