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Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh
Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh
Dissertations and Theses
Artificial Intelligence (AI) has contributed a lot since the beginning. Healthcare is no exception. Detecting anomaly/abnormality in (bio)medical image is crucial. In this thesis, we aim at detecting/screening pulmonary abnormalities due to Covid-19 in chest X-rays using deep features. We study CheXNet, DenseNet169, ResNet50 and VggNet16 to analyze CXRs to detect the evidence of Covid-19 in this research. CheXNet was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). We created a benchmark dataset size of 4,716 CXRs (2,358 Covid-19 positive cases and 2,358 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, using the DenseNet, …
Towards Improving Accuracy And Interpretability Of Deep Learning Based On Satellite Image Classification, Yamile Patino Vargas
Towards Improving Accuracy And Interpretability Of Deep Learning Based On Satellite Image Classification, Yamile Patino Vargas
Dissertations and Theses
ABSTRACT
The study of satellite images provides a way to monitor changes in the surface of the Earth and the atmosphere. Convolutional Neural Networks (CNN) have shown accurate results in solving practical problems in multiple fields. Some of the more recognized fields using CNNs are satellite imagery processing, medicine, communication, transportation, and computer vision. Despite the success of CNNs, there remains a need to explain the network predictions further and understand what the network is determining as valuable information.
There are several frameworks and methodologies developed to explain how CNNs predict outputs and what their internal representations are [1, 4, …