Comparative Analysis Of Deep Learning And Graph Cut Algorithms For Cell Image Segmentation
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Comparative Analysis of Deep Learning and Graph Cut Algorithms for Cell Image Segmentation
Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into multiple regions, normally based on the characteristics of pixels in a given image. Image segmentation could involve separating the foreground from background in an image, or clustering image regions based on similarities in intensity, color, or shape. In this thesis, we consider the problem of cell image segmentation and evaluate the performance of two major techniques on a dataset of cell image sequences. First, we apply a traditional segmentation algorithm based on the so-called graph cut that addresses the segmentation problem using an energy minimization scheme defined on a weighted graph. Second, we use modern techniques based on deep neural networks, namely U-Net and LSTM that have a time-consuming training and a relatively quick testing phase. Performance of each technique will be analyzed qualitatively and quantitatively based on various standard measures and will be compared statistically.
Computational Intelligence for Genomics Data
Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers. - Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing major design challenges, best practices, pitfalls, and research potential - Explores machine and deep learning techniques applied to dimensionality reduction, feature extraction, data selection, and their application in genomics - Presents case studies of various diseases based on gene microarray expression data, including cancer, liver disorders, neuromuscular disorders, and neurodegenerative disorders
Machine Learning and Cybernetics
This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.