Computer-Aided Diagnosis of Endometrial Cancer Histopathologic Images Based on an Improved InceptionNeXt Model

Endometrial cancer is a common female cancer that poses a threat to the health of women worldwide. Several researchers have developed deep learning-based computer-aided diagnosis (CAD) systems for endometrial cancer with good results. However, when finer diagnostic results are needed, the results of existing models are not satisfactory. Therefore, we propose a CAD method for endometrial cancer based on an improved InceptionNeXt network. We designed an efficient coordinate attention module to be added to the network structure, which further enhances the model’s ability to extract location information and channel relationships. We improved the training strategy to effectively improve the generalization ability and robustness of the model. Our model performs well in both four-classification and two-classification tasks, with diagnostic accuracy of 70.74% and 95.43%, respectively. We also performed a visualization analysis of the model to validate its ability to identify morphological features of images and provide pathological interpretations. Our method has a reliable potential to assist physicians in making clinically graded diagnoses.