Final Examination Schedule
PLEASE JOIN US AS THE FOLLOWING CANDIDATES PRESENT THEIR CULMINATING WORK.
Monday, November 28th
Chair: Dr. Sohini Roychowdhury
Candidate: Master of Science in Electrical Engineering
10:30 AM; DISC 464
Investigation of Convolutional Neural Network Architectures for
Image-based Feature Learning and Classification
Convolutional Neural Networks (CNN) are useful methods for identification of previously unknown embedded patterns in images. Several object and facial recognition along with image segmentation tasks have benefited from the non-linear abstraction of hybrid features using CNN. This work presents a novel CNN model parametrization work-flow developed on the cloud-computing platform of Microsoft Azure Machine Learning Studio (MAMLS) that is capable of learning from the feature maps and classifying multi-modal images with different variabilities using one common flow. This two-step work-flow trains CNN models by spliting the data into training and testing data sets. First, the CNN layers are fixed and the optimal kernel and normalization parameters are identified that maximize classification accuracy on the test data. Next, using the optimal kernel and normalization parameters, the best CNN architecture that maximizes classification accuracy is detected. Finally, the activated feature maps (AFMs) from the optimally parameterized CNN model are analyzed to learn new features that can enhance image-based classification accuracies. The proposed flow achieves classification accuracies in the range of 92.5-99.2% that can be further enhanced by doubling the samples based on the features learned from the AFMs. The proposed nondeep CNN models in the MAMLS platform are capable of processing image data sets with 400-4 million samples using a common flow without exponential increase in the computation time. Thus, optimally parametrized non-deep CNN models are capable of identifying novel features that may enhance image-based classification accuracies.
For computed tomography (CT) images, quantitative image assessment can allow for benchmarking image processing methods and optimization of image acquisition parameters. Large volumes of CT images from phantoms and patients are analyzed using the CNN models compare to a baseline model that vary in their implementation time complexities. The goal here is to model the data set variability for prediction of CT image quality (CTIQ). We observe that for 70% of data samples in training and 30% data sample in test set, respectively, the average multi-class classification accuracies for CTIQ prediction varies significantly as the data sets are switched from the phantom to patient images. The CNN model is found to be more suitable for CT image texture classification in the absence of structural variabilities. Our analysis demonstrates that CNN models are consistent identifiers of structural similarities for CT image data sets. Future work on multi-objective CNN modeling and 3D CNN modeling may lead to new insights for classification tasks.