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Gans for data augmentation in healthcare

Solanki, Arun.

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Gans for data augmentation in healthcare

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Gans for data augmentation in healthcare/ edited by Arun Solanki, Mohd Naved.
其他作者:	Solanki, Arun.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	x, 251 p. :illustrations (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Diagnosis - Data processing. -
電子資源:	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:

1966311
Diagnosis
--Data processing.

LC Class. No.: RC78.7.D35

Dewey Class. No.: 616.07540285

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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.
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