Tamil Handwritten Text Recognition is the process of identifying and transcribing handwritten Tamil characters into machine-encoded text. Due to variations in the style, stroke, and shape of characters, identifying ha...
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The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This ...
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Graphics Interchange Format (GIF) encoding is the art of reproducing an image with limited colors. Existing GIF encoding schemes often introduce unpleasant visual artifacts such as banding artifact, dotted-pattern noi...
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Recently, the attention mechanism has been introduced into object tracking, making significant improvements in tracking performance. However, the tracking target often undergoes deformation during tracking, which can ...
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The COVID-19 pandemic has suddenly arisen as a global health emergency requiring concerted international action. Governments are working to stop the virus from spreading and health organizations rushed to create vacci...
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Renal irregularities are serious medicinal condition that is becoming more common and killing more people each year. In its early stages, renal irregularities are curable, but it can progress irreversibly and result i...
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Image processing algorithms, which supply the image quality, are used by modern mobile devices to capture images. These methods need more RAM to process an image and fix these problems. Software pipelines are utilized...
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Advancements in medical imaging have been substantially driven by deep learning technologies, particularly Convolutional Neural Networks (CNNs). A critical hurdle in this domain is the imbalance of datasets, where cer...
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ISBN:
(纸本)9798350383652
Advancements in medical imaging have been substantially driven by deep learning technologies, particularly Convolutional Neural Networks (CNNs). A critical hurdle in this domain is the imbalance of datasets, where certain medical conditions are underrepresented, leading to potential biases in diagnostic models. This research addresses the imbalance in medical imaging datasets, specifically in chest radiography, by leveraging Generative Adversarial Networks (GANs) for data augmentation. The study utilizes the ChestXray2017 dataset, which is skewed towards pneumonia cases, resulting in a dearth of normal chest X-ray images. To counter this, Deep Convolution Generative Adversarial Networks (DCGAN) were employed to generate synthetic images of normal chest X-rays, thus aiming to balance the dataset. In this study, we conducted a comparative analysis of a Convolutional Neural Network's (CNN) performance on a chest radiography dataset, before and after augmenting it with Deep Convolution Generative Adversarial Network (DCGAN)-generated images. Initially, the CNN trained on the un-augmented dataset achieved 93% training accuracy and 87% validation accuracy. After integrating 400 synthetic normal chest X-ray images, the training accuracy slightly increased to 95%, while the validation accuracy notably improved to 89%. This enhancement in validation accuracy demonstrates the model's improved generalization capabilities due to a more balanced training dataset. Our results indicate that GAN-based data augmentation effectively addresses class imbalances in medical imaging datasets, potentially leading to more accurate and reliable diagnostic models. However, the study also underscores the need for further research into the quality and ethical implications of using synthetic images in medical diagnostics. Overall, the integration of GAN-generated images into CNN training presents a promising method for improving classification performance in medical imaging, offering a practical
In the contemporary landscape of online social networks, preserving users' privacy while applying clustering techniques is a pivotal concern. This paper explores the integration of differential privacy into social...
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The provision of feedback that is both effective and constructive is of critical importance in the process of enhancing the shopping mall experience for customers. It is a very useful instrument for identifying areas ...
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