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Full-Text Articles in Physical Sciences and Mathematics
Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu
Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu
LSU Doctoral Dissertations
Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …
Symbolic And Deep Learning Based Data Representation Methods For Activity Recognition And Image Understanding At Pixel Level, Manohar Karki
Symbolic And Deep Learning Based Data Representation Methods For Activity Recognition And Image Understanding At Pixel Level, Manohar Karki
LSU Doctoral Dissertations
Efficient representation of large amount of data particularly images and video helps in the analysis, processing and overall understanding of the data. In this work, we present two frameworks that encapsulate the information present in such data. At first, we present an automated symbolic framework to recognize particular activities in real time from videos. The framework uses regular expressions for symbolically representing (possibly infinite) sets of motion characteristics obtained from a video. It is a uniform framework that handles trajectory-based and periodic articulated activities and provides polynomial time graph algorithms for fast recognition. The regular expressions representing motion characteristics can …
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
LSU Doctoral Dissertations
Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework …