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Hamparsum Bozdogan

Hamparsum Bozdogan

Hamparsum Bozdogan

Toby McKenzie Professor in Business, Information Complexity and in Model Selection at the University of Tennessee

Prof. Hamparsum ‘Ham” Bozdogan is Toby McKenzie Professor in Business, Information Complexity and in Model Selection in the Department of Business Analytics and Statistics, at the Haslam College of Business of the University of Tennessee in Knoxville, Tennessee. He is a nationally and internationally recognized renowned expert in the area of information-theoretic model selection in Akaike School. He is the developer of a new model selection criterion called ICOMP for ‘information complexity’ and others. He has published over 70 research articles in prestigious peer reviewed journal. Edited 6 books. He serves as the Editor, and Editorial Board on 7 journals. He produced over 20 Ph.D., 50 Masters students, and 6 Post-Doctoral Fellows. He is the recipient of many distinguished teaching and research awards.

Track: Artificial Intelligence & Healthcare

Monday, April 15, 3:40–4:30pm

Novel Computer-Aided Detection of Breast Cancer: Stalking the Serial Killer

Breast cancer is the second leading cause of death among women worldwide and, because preventing it is beyond current medical abilities, much research attention has been focused on early detection and post diagnostic treatment. But early detection has flaws. Even mammography, the most effective tool for detecting the cancer, misses up to 30 percent of breast lesions. The missed evidence is attributed to poor-quality radiographic images and eye fatigue and oversight on the part of radiologists who read the images. In this presentation, we present several novel statistical modeling and machine learning techniques for computer-aided detection (CAD) of breast cancer on 1,269 Italian patients by introducing and developing flexible supervised and unsupervised classification methods using information complexity criterion. The efficiency and robustness of our approach is shown in computer-aided diagnostic tools that show promise in increasing the ability to spot cancerous lesions in the digital images collected during mammography.