2022
DOI: 10.1016/j.procs.2022.08.008
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A Novel COVID-19 Detection Model Based on DCGAN and Deep Transfer Learning

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Cited by 11 publications
(4 citation statements)
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“…Therefore, we trained multiple categorization architectures rather than focusing on a single-model architecture to ensure the reliability of the results. Here, 13 CNN models were chosen: Xception [11,[28][29][30], InceptionResNetV2 [31], DenseNet121 [32], MobileNetV2 [33], ResNet101 [34][35][36], VGG16 [37], AlexNet [38], Vgg19 [39], Resnet18 [40], Resnet50 [41], InceptionV3 [42], GoogleNet [43], and ShuffleNet [44]. Deep ensemble-based learning is generally characterized by assembling a set of predictions derived from various deep convolutional neural network (CNN) models.…”
Section: Computermentioning
confidence: 99%
“…Therefore, we trained multiple categorization architectures rather than focusing on a single-model architecture to ensure the reliability of the results. Here, 13 CNN models were chosen: Xception [11,[28][29][30], InceptionResNetV2 [31], DenseNet121 [32], MobileNetV2 [33], ResNet101 [34][35][36], VGG16 [37], AlexNet [38], Vgg19 [39], Resnet18 [40], Resnet50 [41], InceptionV3 [42], GoogleNet [43], and ShuffleNet [44]. Deep ensemble-based learning is generally characterized by assembling a set of predictions derived from various deep convolutional neural network (CNN) models.…”
Section: Computermentioning
confidence: 99%
“…Puttagunta et al also used DCGAN to generate X-ray chest images [ 61 ]. The DCGAN was trained using 934 images and achieved an FID score is 23.78.…”
Section: Related Workmentioning
confidence: 99%
“…DCGAN enhances data by incorporating convolution operations into GAN, creating more realistic images that excel in data augmentation applications. [21][22][23] The paper suggests combining the real-time classification capability of MobileNetV2 with the high-quality data generation ability of DCGAN to create a lightweight hybrid model. This model is designed for the real-time detection and classification of uterine fibroids during surgery, aiming to aid doctors in diagnosis.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning can produce higher‐quality images than traditional methods like random rotation, cropping, and flipping. DCGAN enhances data by incorporating convolution operations into GAN, creating more realistic images that excel in data augmentation applications 21–23 . The paper suggests combining the real‐time classification capability of MobileNetV2 with the high‐quality data generation ability of DCGAN to create a lightweight hybrid model.…”
Section: Introductionmentioning
confidence: 99%