Applications of Convolutional Neural Networks for Classification across Multiple Imaging Domains

Gurman Gill
Dept of Computer Science, Sonoma State University

Stevenson Hall 1300
12:00 PM - 12:50 PM

Machine learning (ML) has become a central technique to solve a wide range of real-world problems. The availability of large data sets combined with the high-speed implementation of algorithms that use various models of the data to reveal the relationship between the input(s) and output(s) makes this approach attractive in all aspects of computational problem-solving. It is used in web search, spam filters, image tagging, and recognition, interactive voice-activated programs like Apple’s Siri, in software that beats human professionals in chess, Jeopardy, Go, etc. Recent deep learning (DL) algorithms have further revolutionized the field and their applications are in diverse fields of medicine, finance, weather, earth sciences, etc. In particular, learning from images has been made possible by constructing deep, layered, and hierarchical models of data, typically represented using Convolutional Neural Networks (CNN). In this talk, I will present research conducted at SSU based on CNNs on projects with varying imaging domains and classification goals: 

1) Automatic Classification of Wild Animals from Camera Trap Images 

2) Identification of Interstitial Lung Disease from Computed Tomography (CT) scans

3) Classification of Sigma Clasts in Microscopic Photomicrographs of Rocks.