A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Khan, Asifullah; Khan, Saddam Hussain; Saif, Mahrukh; Batool, Asiya; Sohail, Anabia; Khan, Muhammad Waleed

doi:10.1080/0952813x.2023.2165724

Cited by 40 publications

(24 citation statements)

References 146 publications

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“…Dropout layers are used with FC layers to minimize the risks of over tting. (4) (5) (6) (7) illustrates the source feature map of size , and the lter with size is de ned by k in the convolutional operator used in Eq. ( 4).…”

Section: Structural Details Of the Developed Res-brnetmentioning

confidence: 99%

Brain tumor MRI Classification using a Novel Deep Residual and Regional CNN

Zahoor

Khan²,

Sadiq

et al. 2022

Preprint

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Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefore, a novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor Magnetic Resonance Imaging (MRI) classification. The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks. Spatial blocks extract the brain tumor’s homogeneity and heterogeneity patterns, and boundary-related features. Additionally, the residual blocks significantly capture local and global texture variations of brain tumors. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances (accuracy: 98.22%, sensitivity: 0.9811, F1-score: 0.9841, and precision: 0.9822) on challenging datasets. Additionally, the performance of the proposed Res-BRNet indicates a strong potential for medical image-based disease analyses.

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Section: Structural Details Of the Developed Res-brnetmentioning

confidence: 99%

Brain tumor MRI Classification using a Novel Deep Residual and Regional CNN

Zahoor

Khan²,

Sadiq

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, the use of CT received attention, and numerous studies were carried out to verify its efcacy as a substitute for identifying infected individuals [10]. Tese studies reveal that most patients' CT images have consolidation spots and ground-glass opacities (GGO), indicating that CT scans may be used to verify the presence of COVID-19 disease and monitor therapeutic progression [10,11]. Te coronavirus spreads quickly between people or when in contact with an area where an infected person resides.…”

Section: Introductionmentioning

confidence: 99%

Deep SVDD and Transfer Learning for COVID‐19 Diagnosis Using CT Images

Alhadad

Mostafa

El-Bakry

2023

Computational Intelligence and Neuroscience

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The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient’s life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333).

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“…In recent years, various life-threatening diseases have been detected and diagnosed using deep learning techniques by a number of researchers [ 28 – 32 ]. Baltruschat et al [ 28 ] conducted a study comparing multiple deep-learning approaches to classify chest X-Ray images with multiple labels.…”

Section: Introductionmentioning

confidence: 99%

“…However, the most effective model was specifically trained using only X-ray images and incorporated non-image data. A systematic survey of deep learning techniques for the analysis of COVID-19 and their usability for detecting Omicron has been provided by [ 32 ]. The COVID-19 pandemic has caused a shift towards utilizing deep learning methods for analyzing and identifying infected areas in radiology images.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection

Mann

Badoni

Soni

et al. 2023

Interdiscip Sci Comput Life Sci

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Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automated using the three convolutional neural network (CNN) models, viz. DenseNet121, ResNet50, and EfficientNetB1 for fast and accurate detection of 14 class labels of thoracic pathology diseases based on chest radiography. These models were evaluated on an AUC score for normal versus abnormal chest radiographs using 112120 chest X–ray14 datasets containing various class labels of thoracic pathology diseases to predict the probability of individual diseases and warn clinicians of potential suspicious findings. With DenseNet121, the AUROC scores for hernia and emphysema were predicted as 0.9450 and 0.9120, respectively. Compared to the score values obtained for each class on the dataset, the DenseNet121 outperformed the other two models. This article also aims to develop an automated server to capture fourteen thoracic pathology disease results using a tensor processing unit (TPU). The results of this study demonstrate that our dataset can be used to train models with high diagnostic accuracy for predicting the likelihood of 14 different diseases in abnormal chest radiographs, enabling accurate and efficient discrimination between different types of chest radiographs. This has the potential to bring benefits to various stakeholders and improve patient care. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12539-023-00562-2.

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A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Cited by 40 publications

References 146 publications

Brain tumor MRI Classification using a Novel Deep Residual and Regional CNN

Brain tumor MRI Classification using a Novel Deep Residual and Regional CNN

Deep SVDD and Transfer Learning for COVID‐19 Diagnosis Using CT Images

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection

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