Why Are Generative Adversarial Networks Vital for Deep Neural Networks? A Case Study on COVID-19 Chest X-Ray Images

Shams, Mahmoud Y.; Elzeki, Omar M.; Elfattah, Mohamed Abd; Medhat, T.; Hassanien, Aboul Ella

doi:10.1007/978-3-030-55258-9_9

Cited by 21 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While, the second dataset aims to develop cutting-edge methods for automatically identifying Adverse Drug Reactions (ADRs) from Twitter data. To achieve this, a dataset consisting of 23,516 rows can be created, where each row represents a tweet that has been categorized as either ADR (1) or Non-ADR (0), based on the presence of drug names, symptoms, and effects. This dataset can enable Company X to monitor ADRs efficiently and accurately in real-time, allowing them to respond promptly to emerging health concerns and protect public health.…”

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing classification of diseases through language model analysis of symptoms

Hassan,

Abd El-Hafeez,

Shams

2024

Sci Rep

View full text Add to dashboard Cite

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

“…In the field of healthcare, accurate and timely diagnosis of diseases is of paramount importance for effective treatment and patient care [1][2][3] . Traditionally, medical professionals rely on their expertise and diagnostic tests to identify diseases based on a patient's symptoms.…”

mentioning

confidence: 99%

Optimizing classification of diseases through language model analysis of symptoms

Hassan,

Abd El-Hafeez,

Shams

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Certainly, data fusion is essential to discover more details and improve observed data's extracted features (Meng et al, 2020;Attallah, Sharkas & Gadelkarim, 2020;Thabtah & Peebles, 2020). Typically, the Generative Adversarial Network (GAN) is widely used for data augmentation, especially for small data presented by Shams et al (2020) for CXR images. In medical applications, the fusion of images is performed to discover essential parts (Tian, Yibing & Fang, 2016).…”

Section: Related Workmentioning

confidence: 99%

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

Elzeki

Elfattah²,

Salem

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

show abstract

“…The majority of them make use of well-known CNN architectures such as VGG [2] , ResNet [3] – [5] , SqueezeNet [3] , [6] , DenseNet [7] and also combine them with decision trees [8] and Support Vector Machines (SVM) [9] . Given the difficulty of obtaining COVID-19 samples, GAN networks have been used [10] , [11] in order to enhance the performance. Moreover, other approaches [12] , [13] based on multi-resolution methods report results that are comparable to those obtained by CNNs.…”

Section: Introductionmentioning

confidence: 99%

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients

et al. 2021

View full text Add to dashboard Cite

Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this paper, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.

show abstract

Why Are Generative Adversarial Networks Vital for Deep Neural Networks? A Case Study on COVID-19 Chest X-Ray Images

Cited by 21 publications

References 15 publications

Optimizing classification of diseases through language model analysis of symptoms

Optimizing classification of diseases through language model analysis of symptoms

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients

Contact Info

Product

Resources

About