Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology - Huang, Wei; Sun, Shasha; Yu, Zhengyu; Lu, Shanshan; Feng, Hao (Frontiers in Pharmacology, 2022)
With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10012 colposcopy images of 1081 patients from Hunan Provincial people’s hospital in China were recorded. Five different colposcopy image features of cervix include chronic cervicitis, intraepithelial lesions, cancer, polypus and free hyperplastic squamous epithelial tissue were extracted to be applied in our deep learning network convolutional neural network model. However, result showed a low accuracy (42.16%) due to computer misrecognition of chronic inflammation, intraepithelial lesions and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant features images - chronic cervicitis and cervical cancer to input into deep learning network. The result indicates high accuracy and robustness with the accuracy of 95.19%, which can be applied to detect whether patient has chronic cervicitis or cervical cancer base on patient’s colposcopy images.