Health and Behavior
Identifying Stages of Lung Cancer through Image Classification using Deep Learning Techniques
Led by Borivoje (Borko) Furht, Ph.D.
Borko Furht is a professor in the Department of Electrical & Computer Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. He is also Director of the NSF-sponsored Industry/University Cooperative Research Center on Advanced Knowledge Enablement at FAU. Before joining FAU, he was a vice president of research and a senior director of development at Modcomp (Ft. Lauderdale), a computer company of Daimler Benz, Germany, a professor at University of Miami in Coral Gables, Florida, and a senior researcher in the Institute Boris Kidric-Vinca, Yugoslavia. Professor Furht received Ph.D. degree in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, video coding and compression, 3D video and image systems, wireless multimedia, medical applications, and cloud computing, and social networks. He is presently Principal Investigator and Co-PI of several multiyear, multimillion dollar projects. He is the author of numerous books and articles in the areas of multimedia, computer architecture, real-time computing, and operating systems. He is a founder and editor-in-chief of the Journal of Multimedia Tools and Applications (Springer) and he recently co-founded Journal of Big Data (Springer). He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Xerox, General Electric, JPL, NASA, Honeywell, and RCA. He has also served as a consultant to various colleges and universities. He has given many invited talks, keynote lectures, seminars, and tutorials. He served as Chairman and Director on the Board of Directors of several high-tech companies and as an expert witness for Cisco, Qualcomm, Adobe, and Bell Canada.
This REU project is focused on classifying different stages on lung cancer through use of a convolutional neural network using open image data set for multi-class classification training. The research training experience will center around data mining and machine learning languages. The associated uses apply to the clinical and medical field with engineering applications.