國立虎尾科技大學 |

Gans for data augmentation in healthcare

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Gans for data augmentation in healthcare/ edited by Arun Solanki, Mohd Naved.
other author:	Solanki, Arun.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	x, 251 p. :illustrations (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Diagnosis - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-43205-7
ISBN:	9783031432057

Gans for data augmentation in healthcare
Gans for data augmentation in healthcare[electronic resource] /edited by Arun Solanki, Mohd Naved. - Cham :Springer International Publishing :2023. - x, 251 p. :illustrations (some col.), digital ;24 cm.

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.

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.

ISBN: 9783031432057

Standard No.: 10.1007/978-3-031-43205-7doiSubjects--Topical Terms:

965713
Diagnosis
--Data processing.

LC Class. No.: RC78.7.D35

Dewey Class. No.: 616.07540285

Gans for data augmentation in healthcare
LDR:03919nam a2200337 a 4500 001 1119901
003 DE-He213
005 20231113204901.0
006 m d
007 cr nn 008maaau
008 240612s2023 sz s 0 eng d
020 $a 9783031432057 $q (electronic bk.)
020 $a 9783031432040 $q (paper)
024 7 $a 10.1007/978-3-031-43205-7 $2 doi
035 $a 978-3-031-43205-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D35
072 7 $a MBG $2 bicssc
072 7 $a UB $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a UXT $2 thema
082 0 4 $a 616.07540285 $2 23
090 $a RC78.7.D35 $b G199 2023
245 0 0 $a Gans for data augmentation in healthcare $h [electronic resource] / $c edited by Arun Solanki, Mohd Naved.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a x, 251 p. : $b illustrations (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Diagnosis $x Data processing. $3 965713
650 0 $a Artificial intelligence $x Medical applications. $3 600038
650 1 4 $a Health Informatics. $3 593963
650 2 4 $a Image Processing. $3 669795
650 2 4 $a Machine Learning. $3 1137723
700 1 $a Solanki, Arun. $e editor. $1 https://orcid.org/0000-0002-4359-406X $3 1354815
700 1 $a Naved, Mohd. $3 1381168
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-43205-7
950 $a Medicine (SpringerNature-11650)