A Novel COVID-19 Detection Model Based on DCGAN and Deep Transfer Learning

Puttagunta, Murali Krishna; Subban, Ravi; C, None Nelson Kennedy Babu

doi:10.1016/j.procs.2022.08.008

Cited by 11 publications

(4 citation statements)

References 13 publications

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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%

Deep Ensemble Learning for Fake Digital Image Detection: A Convolutional Neural Network-Based Approach

Panigrah,

Sethy,

Borra

et al. 2023

RIA

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The advent of deep learning (DL) technologies has paved the way for a plethora of applications, the creation of hyperrealistic images via generative adversarial networks (GANs) being a compelling example. However, these synthetic images, nearly indistinguishable from genuine ones to the human eye, can be exploited for nefarious activities such as cybercrime, extortion, politically motivated campaigns, propaganda, among others. This paper proposes a deep ensemble learning approach to detect such counterfeit images, aiming to mitigate the issues arising from deepfake multimedia. The impetus for engaging ensemble models stems from their capacity to reduce the generalization error of predictions, provided the foundational models exhibit diversity and independence. Consequently, the prediction error diminishes when an ensemble approach is deployed. In this study, the CASIA v2 benchmark datasets, comprising 12,323 color images (5,123 originals and 7,200 counterfeits), were utilized. This investigation employed an ensemble of 13 pre-trained CNN models. The ensemble technique amalgamates these models to form a comprehensive perceptual model, wherein each model contributes to the final outcome. The classification predictions from each model are considered a 'vote', with the majority verdict serving as the final prediction. The proposed methodology was also juxtaposed with prevailing techniques. Assessment of our approach's efficacy revealed a 100% accuracy rate, 97.75% precision, 87.46% recall, and 99.9% AUC, underscoring the improvement offered by the proposed system.

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Section: Computermentioning

confidence: 99%

Deep Ensemble Learning for Fake Digital Image Detection: A Convolutional Neural Network-Based Approach

Panigrah,

Sethy,

Borra

et al. 2023

RIA

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“…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%

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Bajić

Orel

Habijan

2022

Sensors

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Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way. Convolutional neural networks (CNNs) have succeeded in various image processing and classification tasks. Nevertheless, the success of training neural networks in terms of result accuracy and computational requirements requires careful construction of the network layers’ and networks’ parameters. We propose a novel Shallow Convolutional Neural Network (SCNN) architecture for chart-type classification and image generation. We validate the proposed novel network by using it in three different models. The first use case is a traditional SCNN classifier where the model achieves average classification accuracy of 97.14%. The second use case consists of two previously introduced SCNN-based models in parallel, with the same configuration, shared weights, and parameters mirrored and updated in both models. The model achieves average classification accuracy of 100%. The third proposed use case consists of two distinct models, a generator and a discriminator, which are both trained simultaneously using an adversarial process. The generated chart images are plausible to the originals. Extensive experimental analysis end evaluation is provided for the classification task of seven chart classes. The results show that the proposed SCNN is a powerful tool for chart image classification and generation, comparable with Deep Convolutional Neural Networks (DCNNs) but with higher efficiency, reduced computational time, and space complexity.

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“…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%

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

Cai,

Yang,

Xie

et al. 2024

J of Clinical Ultrasound

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PurposeUterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.MethodsA hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real‐time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first‐rate UF images, which are labeled into UF and non‐uterine‐fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.ResultsThe overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real‐time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.ConclusionBy using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

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A Novel COVID-19 Detection Model Based on DCGAN and Deep Transfer Learning

Cited by 11 publications

References 13 publications

Deep Ensemble Learning for Fake Digital Image Detection: A Convolutional Neural Network-Based Approach

Deep Ensemble Learning for Fake Digital Image Detection: A Convolutional Neural Network-Based Approach

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

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