Improvement the Performance of the Chest X-ray Image Classification with Convolutional Neural Network Model by Using Image Augmentations Technique for COVID-19 Diagnosis

Sriwiboon, Nattavut

doi:10.14416/j.kmutnb.2020.10.002

j.kmutnb

2020

DOI: 10.14416/j.kmutnb.2020.10.002

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Improvement the Performance of the Chest X-ray Image Classification with Convolutional Neural Network Model by Using Image Augmentations Technique for COVID-19 Diagnosis

Nattavut Sriwiboon¹

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Principles and Theories of Data Mining With RapidMiner

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Neural network and deep learning techniques are essential tools for data scientists when analyzing big data for forecasting and classification. In supervised learning, data sets are divided into training sets and test sets, and neural network repeatedly adjusts the weight of data to better represent the actual data. This book offers a practical guide to performing a neural network experiment with RapidMiner, which readers can follow step-by-step. For big data, especially non-linear data, deep learning can be employed. This chapter introduces two types of deep learning: convolutional neural networks (CNN) for picture analysis and recurrent neural networks (RNN) for sequential or time series data. The book provides a demonstration of both techniques using RapidMiner, making it accessible to readers who wish to deepen their understanding of these powerful tools.

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Deep Learning

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Principles and Theories of Data Mining With RapidMiner

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Improvement the Performance of the Chest X-ray Image Classification with Convolutional Neural Network Model by Using Image Augmentations Technique for COVID-19 Diagnosis

Cited by 1 publication

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