Publication Date
9-24-2021
Document Type
Article
Publication Title
Computers in Biology and Medicine
Volume
138
DOI
10.1016/j.compbiomed.2021.104874
Abstract
Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results clearly indicate that properly trained learning algorithms can aid in the diagnosis of LGESS.
Keywords
Deep learning, LGESS, Convolutional neural networks, AlexNet, DenseNet, ResNet
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science
Recommended Citation
Xinxin Yang and Mark Stamp. "Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)" Computers in Biology and Medicine (2021). https://doi.org/10.1016/j.compbiomed.2021.104874
Included in
Artificial Intelligence and Robotics Commons, Biomedical Commons, Software Engineering Commons
Comments
This is the Version of Record and can also be read online here.