Enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture

Early detection and treatment are key to improving the prognosis of endometrial cancer. However, conventional machine learning approaches have limited capacity to simulate the complex links between histopathological images and their interpretations, making it challenging to achieve accurate results. A vision transformer-based image classification model has been proposed to assist medical professionals in detecting endometrial cancer and improving patient outcomes. This study aims to develop and evaluate a vision transformer-based model for accurately detecting histopathology images of endometrium, and compare its performance against existing fine-tuning methods such as MobilenetV2, Xception, and VGG16. A publicly accessible histopathology imaging dataset of endometrium was used to train and validate the proposed model. The performance of the model was evaluated against state-of-the-art approaches in the field. The validation results showed that the proposed model attained an accuracy of 99.36%, surpassing the performance of existing fine-tuning methods and achieving the state-of-the-art performance in the widely used endometrial cancer benchmark dataset. These findings highlight the potential of vision transformer-based models in accurately detecting histopathology images of endometrium, which could lead to better patient outcomes. This study highlights the potential of this model as a valuable tool for medical professionals in the early detection and treatment of endometrial cancer, ultimately improving patient outcomes.