Journal Article

Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology

Chen, Yuanyuan, He, Haizhen, Pan, Jiajia, Ye, Yan, Li, Peipei, Ni, Feifei

2025

Overview

Background Cervical cancer remains a significant global health issue, with accurate differentiation between low-grade (LSIL) and high-grade squamous intraepithelial lesions (HSIL) crucial for effective screening and management. Current methods, such as Pap smears and HPV testing, often fall short in sensitivity and specificity. Deep learning models hold the potential to enhance the accuracy of cervical cancer screening but require thorough evaluation to ascertain their practical utility. Methods This study compares the performance of two advanced deep learning models, SEResNet101 and SE-VGG19, in classifying cervical lesions using a dataset of 3,305 high-quality colposcopy images. We assessed the models based on their accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results The SEResNet101 model demonstrated superior performance over SE-VGG19 across all evaluated metrics. Specifically, SEResNet101 achieved a sensitivity of 95%, a specificity of 97%, and an AUC of 0.98, compared to 89% sensitivity, 93% specificity, and an AUC of 0.94 for SE-VGG19. These findings suggest that SEResNet101 could significantly reduce both over- and under-treatment rates by enhancing diagnostic precision. Conclusion Our results indicate that SEResNet101 offers a promising enhancement over existing screening methods, integrating advanced deep learning algorithms to significantly improve the precision of cervical lesion classification. This study advocates for the inclusion of SEResNet101 in clinical workflows to enhance cervical cancer screening protocols, thereby improving patient outcomes. Future work should focus on multicentric trials to validate these findings and facilitate widespread clinical adoption.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Area Under Curve / Artificial intelligence / Biomedical and Life Sciences / Biomedicine / Cancer / Cancer Research / Cancer screening / Cervical cancer / Cervical screening / Classification / Clinical significance / Clinical trials / Colposcopy / Comparative analysis / Datasets / Deep Learning / Diagnosis / Early Detection of Cancer - methods / Female / Health aspects / Health Promotion and Disease Prevention / HSIL / Humans / Iodine / Lesions / LSIL / Medical imaging equipment / Medical prognosis / Medical research / Medical screening / Medicine/Public Health / Oncology / Oncology, Experimental / Papillomavirus infections / Physiological aspects / Precision medicine / Precision Medicine - methods / Public health / ROC Curve / Sensitivity and Specificity / SEResNet101 / Surgical Oncology / Uterine Cervical Dysplasia - diagnosis / Uterine Cervical Dysplasia - pathology / Uterine Cervical Neoplasms - diagnosis / Uterine Cervical Neoplasms - pathology / Women