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
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
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