One of Australia's most common gynaecological cancers could be detected sooner and more accurately thanks to a specialised artificial intelligence (AI) model, new research shows.

The study is published in the journal Computer Methods and Programs in Biomedicine Update.

Researchers from Daffodil International University in Bangladesh, Charles Darwin University, the University of Calgary and Australian Catholic University have developed an AI model which can detect endometrial cancer with 99.26% accuracy.

Endometrial cancer is the most common gynaecological cancer in Australia and one of the most diagnosed cancers in Australian women, according to the Cancer Council.

The model, called ECgMPL, examines histopathological images, which are microscopic images of tissue used in disease analysis. The model enhances the quality of the images, identifies the most important areas and analyses the tissue.

The current endometrial accuracy using automated diagnosis is reported to be approximately 78.91% to 80.93%.

Co-author and CDU Lecturer in Information Technology Dr. Asif Karim said the model could enhance clinical processes.

"The proposed ECgMLP model outperforms existing methods by achieving 99.26 per cent accuracy, surpassing transfer learning and custom models discussed in the research while being computationally efficient," Dr. Karim said.

"Optimised through ablation studies, self-attention mechanisms, and efficient training, ECgMLP generalises well across multiple histopathology datasets, thereby making it a robust and clinically applicable solution for endometrial cancer diagnosis."

Co-author and CDU adjunct Associate Professor Niusha Shafiabady, who is also an Associate Professor at Australian Catholic University, said the model also had benefits outside of endometrial cancer diagnosis.

"The same methodology can be applied for fast and accurate early detection and diagnosis of other diseases which ultimately leads to better patient outcomes," Associate Professor Shafiabady said.

"We evaluated the model on several histopathology image datasets. It diagnosed colorectal cancer with 98.57 per cent accuracy, breast cancer with 98.20 per cent accuracy, and oral cancer with 97.34 per cent accuracy. "The core AI model developed through this research can be adopted as the brain of a software system to be used to assist doctors with decision-making in cancer diagnosis."