Convid-Net: An Enhanced Convolutional Neural Network Framework for COVID-19 Detection from X-Ray Images

Ahmed, Shamsuddin; Hossain, Md. Farhad; Noor, Manan Binth Taj

doi:10.1007/978-981-33-4673-4_55

Cited by 6 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [110], the Convid-Net deep convolutional neural network (CNN) framework for detecting COVID-19 from chest X-ray pictures, which was based on a combination of a residual network and parallel convolution. In the work, the dataset was retrieved from different publicly available sources, consisting of a total of 1440 COVID-19 images, 2470 normal images, and 2407 chest X-ray images of viral and bacterial pneumonia.…”

Section: Custom Deep Learning Techniquesmentioning

confidence: 99%

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

Awassa

Jdey

Dhahri

et al. 2022

Sensors

View full text Add to dashboard Cite

COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

show abstract

Section: Custom Deep Learning Techniquesmentioning

confidence: 99%

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

Awassa

Jdey

Dhahri

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, such layers are effective to extract spatial and temporal features of an image by incorporating filters. Unlike traditional feedforward, these layers contain a considerably reduced number of parameters and employ a weight-sharing and data augmentation approach to reduce the computing requirements (Ahmed, Hossain and Noor 2021). In the proposed work, a Spectral Clustering (SC) approach is used with the proposed algorithm to extract clusters values from the input datasets ).…”

Section: Proposed Modelmentioning

confidence: 99%

A cognitive framework based on deep neural network for classification of coronavirus disease

Kumari

Bhatia

2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Since December 2019, the pandemic of coronavirus (CorV) is spreading all over the world. CorV is a viral disease that results in ill effects on humans and is recognized as public health concern globally. The objective of the paper is to diagnose and prevent the spread of CorV. Spatio-temporal based fine-tuned deep learning model is used for detecting Corv disease so that the prevention measures could be taken on time. Deep learning is an emerging technique that has an extensive approach to prediction. The proposed system presents a hybrid model using chest X-ray images to early identify the CorV suspected people so that necessary action can be taken timely. The proposed work consists of various deep learning neural network algorithms for the identification of CorV patients. A decision model with enhanced accuracy has been presented for early identification of the suspected CorV patients and time-sensitive decision-making. A SQueezeNet model is used for the classification of the CorV patient. An experiment has been conducted for validation purposes to register an average accuracy of 97.8%. Moreover, the outcomes of statistical parameters are compared with numerous state-of-the-art decision-making models in the current domain for performance assessment.

show abstract

“…This semi-supervised method gives better performance compared to supervised learning of CNNs. Other methods based on data augmentation to detect the COVID-19 can be found in Ahmed et al (2021).…”

Section: Data Augmentation and Generation Techniquesmentioning

confidence: 99%

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Hariri

Narin

2021

Soft Comput

View full text Add to dashboard Cite

The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. We further present the databases and major future challenges of DL-based COVID-19 detection. The computed tomography of the chest and X-ray images gives a rich representation of the patient’s lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the preprocessing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pretrained models, generative models, generic neural networks, etc.). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19 and human mobility estimation to limit its spread.

show abstract

Convid-Net: An Enhanced Convolutional Neural Network Framework for COVID-19 Detection from X-Ray Images

Cited by 6 publications

References 11 publications

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

A cognitive framework based on deep neural network for classification of coronavirus disease

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Contact Info

Product

Resources

About