Deep Learning Based Improved Endometrial Cancer Detection Using Comprehensive MRI Dataset

Abstract

Endometrial cancer (EC) needs the correct diagnosis of this cancer during the initial stage, with uterine and tumor boundaries, and magnetic resonance imaging (MRI) represents the clinical gold standard. Nevertheless, all MRI-based EC investigations have three significant limitations that include the unavailability of publicly accessible datasets, the usage of single-sequence or single-plane magnetic resonance imaging, and the unavailability of strong deep learning models to segment the uterus and tumor consistently. In order to fill in these gaps, the proposed paper develops a deep learning model that is a hybrid ResNet-34 + Attention U-Net model, which employs the full diagnostic capabilities of a combination of multi-sequence (nine MRI sequences) and multi-plane (axial, sagittal, and coronal) MRI. One diagnostic center (20212024) was used to construct a large and well-curated local dataset (LD), including normal and stage I EC cases, and two external ones provided by Mao et al. (2022) (MD) and Xiong et al. (2023) (XD) were used only to cross-verify ecological datasets. The decoder has attention gates that boost anatomically weighty areas without reducing the background noises, permitting the precise 3-class division of the background, uterus, and tumor. There were systematic experiments of several encoder-decoder designs, attention, and loss functions trained according to a single training protocol with hyperparameters optimized by Optuna. The proposed model produced the state-of-the-art performance with the overall accuracy of 0.988 on the local dataset, surpassing Mao et al. (0.914) and Xiong et al. (0.869), and a higher Dice coefficient, IoU, precision, recall, and F1-score. Cross-dataset testing also indicated good generalization, with the model proposed showing consistently high accuracy on the MD and XD test sets, which proved that it is strong at different institutions and imaging protocols. The findings validate the proposed structure as a clinically reliable, reproducible, and generalizable solution to EC segmentation at an early stage.