<scp>DC‐GAN</scp>‐based synthetic X‐ray images augmentation for increasing the performance of <scp>EfficientNet</scp> for <scp>COVID</scp>‐19 detection

Shah, Pir Masoom; Ullah, Hamid; Ullah, Rahim; Shah, Dilawar; Wang, Yulin; Islam, Saif ul; Gani, Abdullah; Rodrigues, Joel J. P. C.

doi:10.1111/exsy.12823

Cited by 29 publications

(7 citation statements)

References 25 publications

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“…The author achieved an accuracy of 98.61% when differentiating between NON-COVID 19 (between normal and diseases sample apart from covid cases) and actual COVID 19 samples. Shah et al [25] deep convolutional generative adversarial networks to solve the overfitting problem, which produces bogus images for different classes that includes normal cases, pneumonia, and COVID 19and utilizes the k mean clustering approach with three clusters to evaluate the resulting image accuracy.…”

Section: Related Workmentioning

confidence: 99%

Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray Images

2024

Preprint

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Pneumonia is a serious and life-threatening disease that results in difficulty breathing and needs to be diagnosed as soon as possible. Chest Xray is commonly used to diagnose pneumonia because they are cheap and readily available. Therefore the role of artificial intelligence in radiology has become a hot topic in using deep learning models. To train this model, a huge amount of datasets is required. To overcome this issue data augmentation technique has become a popular method for boosting the training data, particularly in medical images, where the data availability is insufficient. The generative adversarial network has become a popular method to generate synthetic data that looks like real data to increase the model’s performance compared to the traditional augmentation method. This paper focuses on a new GAN architecture i.e, Pneumonia GAN (a.k.a PGAN), to augment chest xray images using generative models. This novel PGAN architecture can successfully increase xray images. The performance of the novel PGAN model generates xray images that look like real images when compared with a conventional method. The comparison result proved that the proposed model generates an excess number of images compared to other techniques, and the mathematical calculation for each layer is discussed.

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Section: Related Workmentioning

confidence: 99%

Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray Images

2024

Preprint

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“…NasNet is one of the CNN architectures consisting of basic building blocks optimized using reinforcement learning [36]- [38]. There are two blocks that must be considered, namely the child block and the parental block.…”

Section: Nasnet-mobilementioning

confidence: 99%

Automated Classification of Multiclass Brain Tumor MRI Images using Enhanced Deep Learning Technique

Razi,

Bustamam,

Latifah

et al. 2023

IJACSA

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The brain is a vital organ, and the brain tumor is one of the most dangerous types of tumors in the world. Neuroimaging is an interesting and important discussion in diagnosing central nervous system tumors. Brain tumors have several types, namely meningioma, glioma, pituitary, schwannoma, and neurocytoma. A radiologist uses magnetic resonance imaging (MRI) to detect brain tumors because of its advantages over computed tomography. However, classifying multiclass MRI is difficult and takes a long time. This study proposes an automated classification of multiclass brain tumors using enhanced deep learning techniques. Various models are used in this research, namely VGG16, NasNet-Mobile, InceptionV3, ResNet50, and EfficientNet. For EfficientNet, we applied EfficientNet-B0-B7. From the experiments, EfficientNet-B2 is the superior, with the highest level of training accuracy of 99.90%, testing accuracy of 99.55%, precision of 99.50%, recall of 99.67%, and F1-Score of 99.58% with a training time of 15 minutes. The development of this automatic classification can assist radiologists in classifying brain tumor types more efficiently.

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“…In the present work, the possibility of applying generative models was investigated as a means of producing synthetic COVID-19 LUS, which could provide a handle for the low data volume available and enable the development of efficient deep learning models. This approach has already been studied for X-ray and CT in other studies ( [14], [15], [16]), but has not yet been explored using LUS exams.…”

Section: Introductionmentioning

confidence: 99%

Application of Generative Adversarial Networks for Synthetic COVID-19 Ultrasound Data Generation

Silva,

Sá,

Félix

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Lung ultrasound emerges as a powerful tool for the diagnosis of COVID-19, being a very cost-effective option to other modalities of exams, such as computerized tomography and Xray imaging. There are efforts in trying to employ deep learning to develop systems that can make an automatic diagnosis based on ultrasound exams to assist the medical decision, but they are limited by the amount of data available. The present work tackles this problem by proposing a method using generative adversarial models to create synthetic data and increase the volume of data to train more complex models. To evaluate whether the synthetic data presents a variety close to that of the original data without replicating training samples, it was devised applications of the Kullback-Leiber divergence and L1 norm. Results indicate that the generated data sampled the main features of the ultrasound data, presenting a variety close to the original data. This points to the possibility of using the proposed method as a means to overcome the problem of low data volume for lung ultrasound. which is shown using expert knowledge.

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DC‐GAN‐based synthetic X‐ray images augmentation for increasing the performance of EfficientNet for COVID‐19 detection

Cited by 29 publications

References 25 publications

Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray Images

Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray Images

Automated Classification of Multiclass Brain Tumor MRI Images using Enhanced Deep Learning Technique

Application of Generative Adversarial Networks for Synthetic COVID-19 Ultrasound Data Generation

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