The rapid proliferation of the Internet of Things is changing industries by making connectivity seamless in nearly every object and letting them exchange data. The major problem inherent to the complexity and decentra...
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Large quantities of data have been generated through various applications such as social media, online platforms, etc., and analyzing these data requires a significant amount of time and effort. Additionally, when enc...
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This paper presents a comprehensive comparison of five state-of-the-art deep learning models for fake currency detection using the Indian Currency Dataset. The performance of Inception V3, ResNet50, Xception and Effic...
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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
One of the most terrible diseases that affect many nations on the globe is lung-related diseases, and early diagnosis of the disease is still a difficult task. It takes time and extra human effort for the oncologists ...
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Convolutional Neural Networks(CNN) are created to work mostly on the image datasets and have revolutionized image classification and object detection by introducing versatile architectures which can be modified accord...
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The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utili...
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In terms of increasing terrorism, criminal behaviour, and anti-social events, there has been a need for safety systems to identify criminals. Face Recognition is one of the vibrant technologies that are very useful fo...
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The agricultural sector faces significant challenges due to plant diseases, which can drastically reduce crop yields and quality. This paper presents a cutting-edge solution for real-time detection and instant remedia...
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Hacking is one of the most widespread issues that the general public faces today. Hackers essentially use some social engineering techniques, combined with the publicly available information, to crack open the social ...
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