Deep Learning for Chest Radiographs enumerates different strategies
implemented by the authors for designing an efficient convolution
neural network-based computer-aided classification (CAC) system for
binary classification of chest radiographs into "Normal" and
"Pneumonia." Pneumonia is an infectious disease mostly caused by a
bacteria or a virus. The prime targets of this infectious disease
are children below the age of 5 and adults above the age of 65,
mostly due to their poor immunity and lower rates of recovery.
Globally, pneumonia has prevalent footprints and kills more
children as compared to any other immunity-based disease, causing
up to 15% of child deaths per year, especially in developing
countries. Out of all the available imaging modalities, such as
computed tomography, radiography or X-ray, magnetic resonance
imaging, ultrasound, and so on, chest radiographs are most widely
used for differential diagnosis between Normal and Pneumonia. In
the CAC system designs implemented in this book, a total of 200
chest radiograph images consisting of 100 Normal images and 100
Pneumonia images have been used. These chest radiographs are
augmented using geometric transformations, such as rotation,
translation, and flipping, to increase the size of the dataset for
efficient training of the Convolutional Neural Networks (CNNs). A
total of 12 experiments were conducted for the binary
classification of chest radiographs into Normal and Pneumonia. It
also includes in-depth implementation strategies of exhaustive
experimentation carried out using transfer learning-based
approaches with decision fusion, deep feature extraction, feature
selection, feature dimensionality reduction, and machine
learning-based classifiers for implementation of end-to-end
CNN-based CAC system designs, lightweight CNN-based CAC system
designs, and hybrid CAC system designs for chest radiographs. This
book is a valuable resource for academicians, researchers,
clinicians, postgraduate and graduate students in medical imaging,
CAC, computer-aided diagnosis, computer science and engineering,
electrical and electronics engineering, biomedical engineering,
bioinformatics, bioengineering, and professionals from the IT
industry.
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