X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network

Al-Itbi, Ali Sami; Alwahhab, Ahmed bahaaulddin A.; Sahan, Ali Mohammed

doi:10.32604/csse.2022.021980

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…The DeTraC model is trained and tested using a combination of two datasets (80 chest X-ray (CXR) images for regular patients and 116 for COVID-19 patients). The experimental results in [70] show that the DeTraC model exhibits a good accuracy of 93.1% (with a sensitivity of 100%) for identifying COVID-19 X-ray images from normal and severe acute respiratory syndrome cases. However, the small dataset (size) used for training and testing the model degraded its performance in complex scenarios.…”

Section: Decompose Transfer and Compose (Detrac)mentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Decompose Transfer and Compose (Detrac)mentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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“…Ali Sami Al-Itbi, Ahmed Bahaaulddin A. Alwahhab, Ali Mohammed Sahan in their study "X-Ray Covid-19 Detection Based on Scatter Wavelet Transform and Dense Deep Neural Network" [4] claimed that the key issues in the present COVID-19 pandemic are the early identification of COVID-19, the cautious separation of infected cases at the lowest cost and curing the disease in the early stages. For that reason, the methodology adopted by authors is imaging tools, particularly computed tomography, which have been critical in diagnosing and treating the disease.…”

Section: Medical Diagnosticsmentioning

confidence: 99%

Method for detecting defects in direct metal deposition using a neural network

Pivkin,

Khodanovich,

Grigoriev

et al. 2024

Laser + Photonics for Advanced Manufacturing

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Research of literature dedicated to neural networks and artificial intelligence was made. The topicality of the research in this area was proven. Neural network models most using in computer vision were chosen. The possibility of using a method to detect defects during the process of direct metal deposition based on neural network application was proven. Three neural network models based on U-Net, ResUNet and VGG-16 architectures were trained. The best of three models was chosen. Goals for future researches were set.

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“…The authors showed that this representation of features is able to significantly improve the image classifiers’ accuracy. A novel COVID-19 detection approach is introduced in Al-Itbi et al (2022) in which the X-ray and CT images are utilized as the inputs. Particularly, a feature extraction method is employed based on the Scatter Wavelet Transform to derive the features of input images and then, the extracted features are used as the inputs of Dense Deep Neural Network to classify the images into COVID-19/Non-COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%