GANs for data augmentation in healthcare / Arun Solanki, Mohd Naved, editors.
Solanki, Arun, 1985-; Naved, Mohd.
2023
RC78.7.D35 G36 2023
Available Online

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Title GANs for data augmentation in healthcare / Arun Solanki, Mohd Naved, editors.
ISBN 9783031432057 electronic book
3031432053 electronic book
3031432045
9783031432040
DOI https://doi.org/10.1007/978-3-031-43205-7
Published Cham : Springer International Publishing AG, 2023.
Language English
Description 1 online resource (x, 251 pages) : illustrations (some color).
Other Standard Identifiers 10.1007/978-3-031-43205-7 doi
Call Number RC78.7.D35 G36 2023
Dewey Decimal Classification 616.07/540285
Summary Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book covers the application of GANs on medical imaging augmentation and segmentation.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Added Author Solanki, Arun, 1985-
Naved, Mohd.
Available in Other Form GANs for Data Augmentation in Healthcare
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro
Contents
Role of Machine Learning in Detection and Classification of Leukemia: A Comparative Analysis
1 Introduction
2 Methodology
3 Literature Review
3.1 Diagnosis by Using SVM, KNN, K-Means, and Naive Bayes
3.2 Diagnosis by Using ANN and CNN Algorithms
3.3 Diagnosis by Using Random Forest and Decision Tree Algorithms
4 Results and Discussion
4.1 K-Means
4.2 Naive Bayes
4.3 Support Vector Means
4.4 Logistic Regression
4.5 XG-Boost
4.6 Accuracy Achieved in Algorithms
5 Conclusion
6 Declarations
References

A Review on Mode Collapse Reducing GANs with GAN's Algorithm and Theory
1 Introduction
2 Literature Survey
3 Conclusion
References
Medical Image Synthesis Using Generative Adversarial Networks
1 Introduction
1.1 Retinal Image Analysis
2 Related Work
3 Proposed Methodology
3.1 Generator Architecture
3.2 Discriminator Architecture
4 Results and Discussion
4.1 Experimental Setup
4.2 Dataset Description
4.3 Performance Metrics
4.4 Results
5 Conclusions
References

Chest X-Ray Data Augmentation with Generative Adversarial Networks for Pneumonia and COVID-19 Diagnosis
1 Introduction
1.1 Related Works
2 GAN Architecture
2.1 Generator Architecture
2.2 Discriminator Architecture
2.3 Classifier
2.4 Batch Size
2.5 Transforms of Training Samples
2.6 Hyperparameters
2.7 Evaluation Metrics
3 Materials and Methods
3.1 Dataset
4 Experimental Details and Results
4.1 Synthetic Images Generated from GAN
5 Discussion and Conclusion
References
State of the Art Framework-Based Detection of GAN-Generated Face Images

1 Introduction
2 Related Work
3 Methodology
3.1 Dataset
3.2 Models Used
3.3 Hardware and Software Setup
3.4 Algorithms
4 Results and Discussions
5 Conclusion
References
Data Augmentation in Classifying Chest Radiograph Images (CXR) Using DCGAN-CNN
1 Introduction
1.1 Data Augmentation
1.2 Augmented Versus Synthetic Data
1.2.1 Augmented Data
1.2.2 Synthetic Data
1.3 Why Data Augmentation Is Important Now?
1.3.1 Improves the Performance of ML Models
1.3.2 Reduces Operation Costs Related to Data Collection
1.4 How Does Data Augmentation Work?

1.5 Advanced Techniques for Data Augmentation
1.6 Data Augmentation in Health Care
1.7 Benefits of Data Augmentation
1.8 Challenges of Data Augmentation
2 GANs
2.1 Introduction
2.2 Why GANs
2.3 Components of GANs
2.4 GAN Loss Function
2.5 Training and Prediction of GANs
2.6 Challenges Faced by GANs
2.7 Different Types of GANs
2.8 Steps to Implement Basic GAN
3 Augmentation of Chest Radiograph Images for Covid-19 Classification
3.1 Methodology
3.1.1 DCGAN
3.1.2 DCGAN Architecture
3.1.3 CNN
3.2 DCGAN-CNN Model's Architecture
4 Conclusion
References

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