Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology

Abstract

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 10,012 colposcopy images of 1,081 patients from Hunan Provincial People’s Hospital in China were recorded. Five different colposcopy image features of the cervix including 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, the result showed a low accuracy (42.16%) due to computer misrecognition of chronic cervicitis, intraepithelial lesions, and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant feature images: chronic cervicitis and cervical cancer to input into a deep learning network. The result indicates high accuracy and robustness with an accuracy of 95.19%, which can be applied to detect whether the patient has chronic cervicitis or cervical cancer based on the patient’s colposcopy images.

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

“…The sensitivity has transitioned from approximately 70% before 2015 to levels surpassing 90% since 2018. Ongoing research employing DL methodologies [22][23][24][26][27][28][29][30][31]34,37,38 has exhibited encouraging outcomes, attaining AUC, precision, and sensitivity metrics exceeding 90%. This has revitalized the enthusiasm for applying DL in this field.…”

Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

“…Experiments demonstrated that on 5455 LSIL+ (including cervical intraepithelial neoplasia and cervical cancer) colposcopic post-acetic-acid images, the proposed model’s accuracy, specificity, sensitivity, and dice coefficient were all greater than those of the popular segmentation model, at 93.04%, 96.00%, 74.78%, and 73.71%, respectively. In 2022, Huang et al [ 13 ] published a paper on five different cervical colposcopy imaging features, including cancer, polyps, intraepithelial lesions, and free hyperplastic squamous epithelial tissue. These were taken out in order to be used in their convolutional neural network model for deep learning networks.…”