首页|Researcher at Department of Computational Intelligence Has Published New Study F indings on Pattern Recognition and Artificial Intelligence (Medical Image Segmen tation Using Grey Wolf-Based U-Net with Bi-Directional Convolutional LSTM)
Researcher at Department of Computational Intelligence Has Published New Study F indings on Pattern Recognition and Artificial Intelligence (Medical Image Segmen tation Using Grey Wolf-Based U-Net with Bi-Directional Convolutional LSTM)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on pa ttern recognition and artificial intelligence. According to news reporting from Chennai, India, by NewsRx journalists, research stated, “In recent years, deep l earning-based networks have been able to achieve state-of-the-art performance in medical image segmentation.” Our news reporters obtained a quote from the research from Department of Computa tional Intelligence: “U-Net, one of the currently available networks, has proven to be effective when applied to the segmentation of medical images. A Convoluti onal Neural Network’s (CNN) performance is heavily dependent on the network’s ar chitecture and associated parameters. There are many layers and parameters that need to be set up in order to manually create a CNN, making it a complex procedu re. Designing a network is made more difficult by using a variety of connections to increase the network’s complexity. Evolutionary computation can be used to s et the parameters of CNN and/or organize the CNN layers as an optimization strat egy. This paper proposes an automatic evolutionary method for detecting an optim al network topology and its parameters for the segmentation of clinical image us ing Grey Wolf Optimization algorithm.”
Department of Computational IntelligenceChennaiIndiaAsiaMachine LearningPattern Recognition and Artificial Int elligence
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