Application of EfficientNet‐B0 and GRU‐based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions

Abstract

Background: Colposcopy is indispensable for the diagnosis of cervical lesions.However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods:The images were collected from 6002 colposcopy examinations of normal control, low-grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic-acid images were all collected.We built a new neural network classification model based on the hybrid algorithm. EfficientNet-b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results:The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion:Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

“…Case F has comparable sensitivity to other Experiments. Experiments 1 through 4 have a higher specificity than Case F. Experiment 3 exhibited the highest precision, sensitivity, and specificity levels [33].…”

“…As for the sensitivity Case A has a higher value compared to Experiment 1, experiment 2, and Experiment 4 while Experiment 3 has 0.1082 higher sensitivity compared to Case A, which is 12.38% higher. In terms of specificity, all the experiments, except for experiment 2, have a higher value compared to Case A, while experiments 3 and 4 the perfect specificity and experiment 1 has 2.31% higher compared to Case A [33].…”

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

“…Tan et al [32] developed a CNN-based TCT cervical-cancer screening model that improved speed and accuracy and overcame the shortage of medical resources required for cervical cancer screening. One study of colposcopy conducted by Chen et al [33] showed that AI has the potential to assist in colposcopies for the accurate diagnosis of cervical disease and early therapeutic intervention in cervical precancer. Despite the promising performance of AI with colposcopy imaging and TCT, there are still shortcomings.…”

Explainable AI Models on Radiographic Images Integrated with Clinical Measurements: Prediction for Unstable Hips in Infants

“…Setting A: Pure CNNs As popular investigating models, E cientNet B0, B4, and B8 models were investigated, with their initial parameters ported from ImageNet-pretrained models [29][30][31][32] . A sigmoid function was used for activation, and binary cross-entropy loss was used to train the NN 33 .…”