CervicoXNet: an automated cervicogram interpretation network

This study proposed an automated cervicogram interpretation using explainable convolutional neural networks named “CervicoXNet” to support medical workers decision. The total number of 779 cervicograms was used for the learning process: 487 w ith VIA ( +) and 292 with VIA ( −). We performed data augmentation process under a geometric transformation scenario, such process produces 7325 cervicogram with VIA ( −) and 7242 cervicogram with VIA ( +). The proposed model outperformed other deep learning models, with 99.22% accuracy, 100% sensitivity, and 98.28% specificity. Moreover, to test the robustness of the proposed model, colposcope images used to validate the model’s generalization ability. The results showed that the proposed architecture still produced satisfactory performance, with 98.11% accuracy, 98.33% sensitiv ity, and 98% specificity. It can be proven that the proposed model has been achieved satisfactory results. To make the prediction results visually interpretable, the results are localized with a heat map in fine-grained pixels using a combination of Grad-CAM and guided backpropagation. CervicoXNet c an be used an alternative early screening tool with VIA alone.Graphical Abstract  
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research