GANs for Data Augmentation in Healthcare

Name: GANs for Data Augmentation in Healthcare
Brand: Springer, Berlin
SKU: 9783031432040
Price: 168.35 EUR
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Автор: Arun Solanki, Mohd Naved

Език:

Корици: С твърди корици

Издател: Springer, Berlin

Наличност: Външен склад

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Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an imp...

Информация за книгата

Автор

Arun Solanki, Mohd Naved

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2023

страници

255

EAN

9783031432040

Enbook ID

43844348

Издател

Springer, Berlin

Теглоt

544

Размери

155 x 235

Пълно описание

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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GANs for Data Augmentation in Healthcare

Информация за книгата

Пълно описание

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