GANs for Data Augmentation in Healthcare / edited by Arun Solanki, Mohd Naved.

Format:

Book

Author/Creator:

Solanki, Arun.

Contributor:

Naved, Mohd.

Series:

Medicine Series

Language:

English

Subjects (All):

Medical informatics.

Image processing.

Machine learning.

Health Informatics.

Image Processing.

Machine Learning.

Local Subjects:

Health Informatics.

Image Processing.

Machine Learning.

Physical Description:

1 online resource (255 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

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.

Contents:

Chapter. 1. Role of Machine learning in Detection and Classification of Leukemia: A Comparative Analysis

Chapter. 2. A Review on Mode Collapse Reducing GANs with GAN’s Algorithm and Theory

Chapter. 3. Medical Image Synthesis using Generative Adversarial Networks

Chapter. 4. Chest X-ray data augmentation with Generative Adversarial Networks for pneumonia and COVID diagnosis

Chapter. 5. State of the Art Framework based Detection of GAN Generated Face Images

Chapter. 6. Data Augmentation in Classifying Chest Radiograph Images (CXR) using DCGAN-CNN

Chapter. 7. Data Augmentation Approaches Using Cycle Consistent Adversarial Networks

Chapter. 8. Geometric Transformations-based Medical Image Augmentation

Chapter. 9. Generative Adversarial Learning for Medical Thermal Imaging Analysis

Chapter. 10. Improving Performance of a Brain Tumor Detection on MRI Images using DCGAN-based Data Augmentation and Vision Transformer(ViT) Approach

Chapter. 11. Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance

Chapter. 12. GAN for Augmenting Cardiac MRI Segmentation

Chapter. 13. WGAN for Data Augmentation

Chapter. 14. Image Segmentation in Medical Images by Using Semi - Supervised Methods.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Solanki, Arun GANs for Data Augmentation in Healthcare

ISBN:

3-031-43205-3

OCLC:

1409702938