SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

Abugabah, Ahed; Al-smadi, Ahmad Mohammad; Almotairi, Sultan

doi:10.1016/j.imu.2022.101059

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…In their framework, augmentation is used to enlarge the dataset [12]. When SEL-COVIDNET [13] is compared to the results of tuned DenseNet121, InceptionResNetV2, and MobileNetV3 models, it outperforms the others in multiclass classification. Deep learning techniques [14] are preferred to artificial intelligence (AI), because overfitting is a significant problem in AI-based systems and many AI-based models are unable to produce the best results.…”

Section: Et Al Published "Automatic Detection Of Coronavirus (Covid-1...mentioning

confidence: 99%

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

2023

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COVID-19 had a global impact, claiming many lives and disrupting healthcare systems even in many developed countries. Various mutations of the severe acute respiratory syndrome coronavirus-2, continue to be an impediment to early detection of this disease, which is vital for social well-being. Deep learning paradigm has been widely applied to investigate multimodal medical image data such as chest X-rays and CT scan images to aid in early detection and decision making about disease containment and treatment. Any method for reliable and accurate screening of COVID-19 infection would be beneficial for rapid detection as well as reducing direct virus exposure in healthcare professionals. Convolutional neural networks (CNN) have previously proven to be quite successful in the classification of medical images. A CNN is used in this study to suggest a deep learning classification method for detecting COVID-19 from chest X-ray images and CT scans. Samples from the Kaggle repository were collected to analyse model performance. Deep learning-based CNN models such as VGG-19, ResNet-50, Inception v3 and Xception models are optimized and compared by evaluating their accuracy after pre-processing the data. Because X-ray is a less expensive process than CT scan, chest X-ray images are considered to have a significant impact on COVID-19 screening. According to this work, chest X-rays outperform CT scans in terms of detection accuracy. The fine-tuned VGG-19 model detected COVID-19 with high accuracy-up to 94.17% for chest X-rays and 93% for CT scans. This work thereby concludes that VGG-19 was found to be the best suited model to detect COVID-19 and chest X-rays yield better accuracy than CT scans for the model.

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Section: Et Al Published "Automatic Detection Of Coronavirus (Covid-1...mentioning

confidence: 99%

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

2023

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“…Besides, the security systems are increasing rapidly, while vein recognition, which is one of the biometric system identification methods, has become an authentic identification method [21]. Convolutional neural networks (CNNs) are considered one of the neural network types utilised for strong correlation data modelling, such as the studies of the earth, time series multivariate, and images [22], [23], [24], [25]. Moreover, the CNNs have achieved significant results in terms of image classification and object detection [26].…”

Section: Methodsmentioning

confidence: 99%

The Effect of Pre-processing on a Convolutional Neural Network Model for Dorsal Hand Vein Recognition

Tarawneh,

Saber,

Almaghthawi

et al. 2024

IJACSA

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There are numerous techniques for identifying users, including cards, passwords, and biometrics. Emerging technologies such as cloud computing, smart gadgets, and home automation have raised users' awareness of the privacy and security of their data. The current study aimed to utilise the CNN model augmented with various pre-processing filters to create a reliable identification system based on the DHV. In addition, the proposed implementing several pre-processing filters to enhance CNN recognition accuracy. The study used a dataset of 500 hand-vein images extracted from 50 patients, while the dataset training was done using the data augmentation technique. The accuracy of the proposed model in this study in classifying images without using image processing showed that 70% was approved for training. Moreover, the results indicated that using the mean filter to remove the noise gave better results, as the accuracy reached 99% in both training conditions.

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“…The building blocks of ResNet50 utilize residual connections, or skip connections, to propagate information directly through the network and overcome the degradation and vanishing gradient problems [41]. MobileNetV3, introduced in 2019, is the latest version of lightweight MobileNets [45], [46]. MobileNetV3 was built upon the MobileNetV1 and V2 structures.…”

Section: ) Pre-trained Cnns -Resnet50 Mobilenetv3small and Mobilenetv...mentioning

confidence: 99%

An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

Mayrose,

Sampathila,

Bairy

et al. 2024

IEEE Access

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Dengue fever is a rapidly increasing mosquito-borne ailment spread by the virus DENV in the tropics and subtropics worldwide. It is a significant public health problem and accounts for many deaths globally. Implementing more effective methods that can more accurately detect dengue cases is challenging. The theme of this digital pathology-associated research is automatic dengue detection from peripheral blood smears (PBS) employing deep learning (DL) techniques. In recent years, DL has been significantly employed for automated computer-assisted diagnosis of various diseases from medical images. This paper explores pre-trained convolution neural networks (CNNs) for automatic dengue fever detection. Transfer learning (TL) is executed on three state-of-the-art CNNs -ResNet50, MobileNetV3Small, and MobileNetV3Large, to customize the models for differentiating the dengueinfected blood smears from the healthy ones. The dataset used to design and test the models contains 100x magnified dengue-infected and healthy control digital microscopic PBS images. The models are validated with a 5-fold crossvalidation framework and tested on unseen data. An explainable artificial intelligence (XAI) approach, Gradientweighted Class Activation Mapping (GradCAM), is eventually applied to the models to allow visualization of the precise regions on the smears most instrumental in making the predictions. While all three transferred pre-trained CNN models performed well (above 98% overall classification accuracy), MobileNetV3Small is the recommended model for this classification problem due to its significantly less computationally demanding characteristics. Transferred pre-trained CNN based on MobileNetV3Small yielded Accuracy, Recall, Specificity, Precision, F1 Score, and Area Under the ROC Curve (AUC) of 0.982 ± 0.011, 0.973 ± 0.027, 0.99 ± 0.013, 0.989 ± 0.015, 0.981 ± 0.012 and 0.982 ± 0.012 respectively, averaged over the five folds on the unseen dataset. Promising results show that the developed models have the potential to provide high-quality support to haematologists by expertly performing tedious, repetitive, and time-consuming tasks in hospitals and remote/low-resource settings.INDEX TERMS Deep learning, dengue fever, digital pathology, explainable artificial intelligence, GradCAM, peripheral blood smear, pre-trained CNN, transfer learning.

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SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

Cited by 11 publications

References 52 publications

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

The Effect of Pre-processing on a Convolutional Neural Network Model for Dorsal Hand Vein Recognition

An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

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