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• W3210928118 abstract "In recent years, several models based on deep learning have been proposed for the identification of Pneumonia from X-ray image of lungs. Lack of datasets with appropriate number of training images is the major challenge faced by these automated models. In this paper, we propose a model called PneumoGAN that not only augments the training dataset by generating enough number of chest X-ray images from random noise but also has the ability to detect pneumonia from a previously unseen image. The proposed model is inspired from Generative Adversarial Networks (GANs). The discriminator of the proposed PneumoGAN model involves five layers while the generator has six layers in it. The experimental results demonstrate the fact that PneumoGAN has precision, recall and F1 score of 87.71%, 91.4% and 89.52% respectively on benchmark datasets. Moreover, an AUC value of 85% is yielded by the proposed approach. Hence, the proposed model helps doctors to speed up the diagnosis process and narrowing the time required to determine whether a person is a pneumonia victim." @default.
• W3210928118 created "2021-11-08" @default.
• W3210928118 creator A5001813486 @default.
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• W3210928118 date "2021-09-02" @default.
• W3210928118 modified "2023-09-26" @default.
• W3210928118 title "PneumoGAN: A GAN based Model for Pneumonia Detection" @default.
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• W3210928118 doi "https://doi.org/10.1109/access51619.2021.9563341" @default.
• W3210928118 hasPublicationYear "2021" @default.
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