Titlebook: GANs for Data Augmentation in Healthcare; Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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?konomie der Abwasserbeseitigungisely adopt these tools and applications. A thorough and organized overview of the ML-based leukemia detection and classification models is provided in this chapter. The early picture of leukemia can be processed using various ML/AI algorithm applications. The purpose is to improve accuracy, reduce

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heterodox

Zusammenfassung und Schlu?folgerungen The inception-based model topped the list with a test accuracy of 99%. The ResNet and EfficientNet models were tied for second place with 97% testing accuracy. A separate five-fold-cross-validation method was also performed in comparison to the holdout method. Though this is a specific use case, we

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Fazit: Die Zukunft der Forschung,ion-based data augmentation segments the infected area and the classification process is proposed to highlight the severity of the disease. The proposed suggests an impartial and all-encompassing framework of evaluation for various information augmentation techniques. With this cutting-edge procedur

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,Etablierte Gesch?ftsmodelle von Agenturen,roach, a large artificial dataset of images can be generated from a less number of images, which could be very helpful for the diagnosis of any disease. These images are artificially created; there are no original patient records or privacy issues. The easy sharing of data among hospitals and diagno

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Besonderheiten der Bauproduktion,ructures of varied shapes and sizes while suppressing irrelevant areas. On CMRI pictures from new suppliers and centers, the suggested augmentation and consistency training strategy indicated increased performance. When tested on CMRI data from four suppliers and six clinical centers, our technique