Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Kumar, Pankaj; Bajpai, Bhavna; Gupta, Deepak; Jain, Dinesh; Vimal, S.

doi:10.1108/wje-12-2020-0655

Cited by 9 publications

(5 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is an approach where the features are automatically extracted from the data. These automatically extracted features have shown promising results in speech recognition (Noda et al, 2015), image retrieval (Mahajan and Chaudhary, 2019), hand gesture recognition (Shanmuganathan et al, 2020), object classification (Robinson et al, 2020), genome analysis (Ramamurthy et al, 2020), COVID-19 detection (Poongodi et al, 2021;Nayak et al, 2021;Kumar et al, 2021), diabetic retinopathy (Thomas et al, 2021(Thomas et al, , 2020 and other biomedical applications (Ravi et al, 2017). In recent years, some groups have presented epileptic seizure detection system using deep learning techniques (Gupta et al, 2021;Mahajan et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Automatic epileptic seizure detection using LSTM networks

Shekokar¹,

Dour

2021

WJE

View full text Add to dashboard Cite

Purpose The purpose of this work is to make a computer aided detection system for epileptic seizures. Epilepsy is a neurological disorder characterized as the recurrence of two or more unprovoked seizures. The common and significant tool for aiding in the identification of epilepsy is Electroencephalography (EEG). The EEG signals contain information about the electrical activity of the brain. Conventionally, clinicians study the EEG waveforms manually to detect epileptic abnormalities, which is very time-consuming and error-prone. Design/methodology/approach The authors have presented a three-layer long short-term memory network for the detection of epileptic seizures. Findings The network classifies between seizure and non-seizure with 99.5% accuracy only in 30 epochs. This makes the proposed methodology useful for real-time seizure detection. Originality/value This research work is original and not plagiarized.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic epileptic seizure detection using LSTM networks

Shekokar¹,

Dour

2021

WJE

View full text Add to dashboard Cite

show abstract

“…Though this matrix is able to recover the signals, the effect of different noises on reconstruction quality is not tested. Moreover researchers have proposed various machine leaning models such as convolutional neural network, Darknet for different applications (Poongodi, Hamdi et al , 2021; Kumar et al , 2021; Shanmuganathan et al , 2020; Adhumitha Ramamurthy et al , 2020; Robinson et al , 2020; Thomas et al , 2021).…”

Section: Related Workmentioning

confidence: 99%

Machine learning improvements to compressive sensing for speech enhancement in hearing aid applications

Vanjari

Kolte

2021

WJE

View full text Add to dashboard Cite

Purpose Speech is the primary means of communication for humans. A proper functioning auditory system is needed for accurate cognition of speech. Compressed sensing (CS) is a method for simultaneous compression and sampling of a given signal. It is a novel method increasingly being used in many speech processing applications. The paper aims to use Compressive sensing algorithm for hearing aid applications to reduce surrounding noise. Design/methodology/approach In this work, the authors propose a machine learning algorithm for improving the performance of compressive sensing using a neural network. Findings The proposed solution is able to reduce the signal reconstruction time by about 21.62% and root mean square error of 43% compared to default L2 norm minimization used in CS reconstruction. This work proposes an adaptive neural network–based algorithm to enhance the compressive sensing so that it is able to reconstruct the signal in a comparatively lower time and with minimal distortion to the quality. Research limitations/implications The use of compressive sensing for speech enhancement in a hearing aid is limited due to the delay in the reconstruction of the signal. Practical implications In many digital applications, the acquired raw signals are compressed to achieve smaller size so that it becomes effective for storage and transmission. In this process, even unnecessary signals are acquired and compressed leading to inefficiency. Social implications Hearing loss is the most common sensory deficit in humans today. Worldwide, it is the second leading cause for “Years lived with Disability” the first being depression. A recent study by World health organization estimates nearly 450 million people in the world had been disabled by hearing loss, and the prevalence of hearing impairment in India is around 6.3% (63 million people suffering from significant auditory loss). Originality/value The objective is to reduce the time taken for CS reconstruction with minimal degradation to the reconstructed signal. Also, the solution must be adaptive to different characteristics of the signal and in presence of different types of noises.

show abstract

“…Kumar et al (2021) proposed a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.…”

Section: Special Issue (Part 1) On Computer-aided Learning and Analys...mentioning

confidence: 99%

“…A novel similarity measure-based random forest classifier is proposed to increase the efficiency of the framework. Kumar et al (2021) proposed a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images. Ch et al (2021) summarized and give an overview of the present preclinical research and clinical trials of potential candidates for COVID-19 treatments and vaccines.…”

mentioning

confidence: 99%

Guest editorial

Dhiman

2022

WJE

View full text Add to dashboard Cite

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Cited by 9 publications

References 50 publications

Automatic epileptic seizure detection using LSTM networks

Automatic epileptic seizure detection using LSTM networks

Machine learning improvements to compressive sensing for speech enhancement in hearing aid applications

Guest editorial

Contact Info

Product

Resources

About