001484182 000__ 06610cam\\22005897i\4500
001484182 001__ 1484182
001484182 003__ OCoLC
001484182 005__ 20240117003316.0
001484182 006__ m\\\\\o\\d\\\\\\\\
001484182 007__ cr\cn\nnnunnun
001484182 008__ 231118s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001484182 019__ $$a1409546478
001484182 020__ $$a9783031432057$$qelectronic book
001484182 020__ $$a3031432053$$qelectronic book
001484182 020__ $$z3031432045
001484182 020__ $$z9783031432040
001484182 0247_ $$a10.1007/978-3-031-43205-7$$2doi
001484182 035__ $$aSP(OCoLC)1409702938
001484182 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dYDX
001484182 049__ $$aISEA
001484182 050_4 $$aRC78.7.D35$$bG36 2023
001484182 08204 $$a616.07/540285$$223/eng/20231128
001484182 24500 $$aGANs for data augmentation in healthcare /$$cArun Solanki, Mohd Naved, editors.
001484182 264_1 $$aCham :$$bSpringer International Publishing AG,$$c2023.
001484182 300__ $$a1 online resource (x, 251 pages) :$$billustrations (some color).
001484182 336__ $$atext$$btxt$$2rdacontent
001484182 337__ $$acomputer$$bc$$2rdamedia
001484182 338__ $$aonline resource$$bcr$$2rdacarrier
001484182 504__ $$aIncludes bibliographical references.
001484182 5058_ $$aIntro -- Contents -- Role of Machine Learning in Detection and Classification of Leukemia: A Comparative Analysis -- 1 Introduction -- 2 Methodology -- 3 Literature Review -- 3.1 Diagnosis by Using SVM, KNN, K-Means, and Naive Bayes -- 3.2 Diagnosis by Using ANN and CNN Algorithms -- 3.3 Diagnosis by Using Random Forest and Decision Tree Algorithms -- 4 Results and Discussion -- 4.1 K-Means -- 4.2 Naive Bayes -- 4.3 Support Vector Means -- 4.4 Logistic Regression -- 4.5 XG-Boost -- 4.6 Accuracy Achieved in Algorithms -- 5 Conclusion -- 6 Declarations -- References
001484182 5058_ $$aA 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
001484182 5058_ $$aChest X-Ray Data Augmentation with Generative Adversarial Networks for Pneumonia and COVID-19 Diagnosis -- 1 Introduction -- 1.1 Related Works -- 2 GAN Architecture -- 2.1 Generator Architecture -- 2.2 Discriminator Architecture -- 2.3 Classifier -- 2.4 Batch Size -- 2.5 Transforms of Training Samples -- 2.6 Hyperparameters -- 2.7 Evaluation Metrics -- 3 Materials and Methods -- 3.1 Dataset -- 4 Experimental Details and Results -- 4.1 Synthetic Images Generated from GAN -- 5 Discussion and Conclusion -- References -- State of the Art Framework-Based Detection of GAN-Generated Face Images
001484182 5058_ $$a1 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?
001484182 5058_ $$a1.5 Advanced Techniques for Data Augmentation -- 1.6 Data Augmentation in Health Care -- 1.7 Benefits of Data Augmentation -- 1.8 Challenges of Data Augmentation -- 2 GANs -- 2.1 Introduction -- 2.2 Why GANs -- 2.3 Components of GANs -- 2.4 GAN Loss Function -- 2.5 Training and Prediction of GANs -- 2.6 Challenges Faced by GANs -- 2.7 Different Types of GANs -- 2.8 Steps to Implement Basic GAN -- 3 Augmentation of Chest Radiograph Images for Covid-19 Classification -- 3.1 Methodology -- 3.1.1 DCGAN -- 3.1.2 DCGAN Architecture -- 3.1.3 CNN -- 3.2 DCGAN-CNN Model's Architecture -- 4 Conclusion -- References
001484182 506__ $$aAccess limited to authorized users.
001484182 520__ $$aComputer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book covers the application of GANs on medical imaging augmentation and segmentation.
001484182 650_6 $$aDiagnostics$$xInformatique.
001484182 650_6 $$aIntelligence artificielle en médecine.
001484182 650_0 $$aDiagnosis$$xData processing.
001484182 650_0 $$aArtificial intelligence$$xMedical applications.$$xMedical applications$$0(DLC)sh 88003000
001484182 655_0 $$aElectronic books.
001484182 7001_ $$aSolanki, Arun,$$d1985-
001484182 7001_ $$aNaved, Mohd.
001484182 77608 $$iPrint version:$$aSolanki, Arun$$tGANs for Data Augmentation in Healthcare$$dCham : Springer International Publishing AG,c2023$$z9783031432040
001484182 852__ $$bebk
001484182 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43205-7$$zOnline Access$$91397441.1
001484182 909CO $$ooai:library.usi.edu:1484182$$pGLOBAL_SET
001484182 980__ $$aBIB
001484182 980__ $$aEBOOK
001484182 982__ $$aEbook
001484182 983__ $$aOnline
001484182 994__ $$a92$$bISE