Using Artificial Intelligence techniques to detect pancreatic cancer early
Faculty Department
Department of Mathematics and Computer Science
Presentation Type
Powerpoint
Location
Larini Room
Start Date
25-2-2026 9:55 AM
End Date
25-2-2026 10:10 AM
Description (Abstract)
In this presentation, we will present some key points of artificial intelligence (AI), specifically about Deep Learning models. From there, we will present some applications of these models in supporting the early detection of pancreatic cancer. The aggressive aggressiveness and sometimes silent beginning of pancreatic cancer make early identification a significant problem. In this research, we present RIDT-RLC, an innovative ensemble approach to pancreatic cancer diagnosis that combines a random indexive decision tree with a reinforcement learning classifier. The presentation also presents some recent research results of the author group that have been published in good peer-reviewed journals in the SCIE/Scopus category on these topics.
Keywords
Artificial Intelligence, Artificial Neural Networks, Deep Learning, Disease Diagnosis, pancreatic cancer
Related Pillar(s)
Community, Study
Using Artificial Intelligence techniques to detect pancreatic cancer early
Larini Room
In this presentation, we will present some key points of artificial intelligence (AI), specifically about Deep Learning models. From there, we will present some applications of these models in supporting the early detection of pancreatic cancer. The aggressive aggressiveness and sometimes silent beginning of pancreatic cancer make early identification a significant problem. In this research, we present RIDT-RLC, an innovative ensemble approach to pancreatic cancer diagnosis that combines a random indexive decision tree with a reinforcement learning classifier. The presentation also presents some recent research results of the author group that have been published in good peer-reviewed journals in the SCIE/Scopus category on these topics.
Short Biography
Helen Dang earned PhDs in Computer Science (2010) and Business Administration (2013). She is currently a Asst. Prof. of Computer Science at Dept. of Mathematics and Computer Science, Molloy University. She is the author or editor of six books and approximately 60 papers in peer-reviewed journals. In addition, she is a PI/Co-PI or member of many national, ministerial, and corporate projects to solve real-world problems. Her research interests include Artificial Intelligence, Artificial Neural Networks, Deep Learning, Data analytics and some healthcare problems.