A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network

Sani, S. M.; Shermeh, Hossein Ebrahimzadeh

doi:10.1016/j.eswa.2022.116740

Cited by 13 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This way, several deep CNNs, like VGG, ResNet, Xception, ShuffleNet, etc., have been employed on the COVID-19 dataset and achieved accuracy of 87% to 98% (Ali Narin et al, 2020 , Shan et al, 2020 , Wang et al, 2020 ). However, the aforementioned techniques have been employed for COVID-19 infection detection but lack analysis information ( Sani & Shermeh, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs

Khan¹,

Iqbal²,

Hassnain

et al. 2023

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs

Khan¹,

Iqbal²,

Hassnain

et al. 2023

Expert Systems with Applications

View full text Add to dashboard Cite

“…For k ≤ 3, the probability proportion in the negative to positive form is 1:1, 2:1, or 1:2. Note that RANkSAT can employ α 3,1 RANkSAT , α 3,2 RANkSAT and α 3,2,1 RANkSAT where the formulation is shown in the Equations ( 1)-(3):…”

Section: Random K Satisfiability (Ranksat)mentioning

confidence: 99%

“…From Equation (4), the literals (positive or negative) are set at random. Equations ( 5)- (7) show examples of α 3,1 RANkSAT , α 3,2 RANkSAT and α 3,2,1 RANkSAT :…”

Section: Random K Satisfiability (Ranksat)mentioning

confidence: 99%

“…from the equations above, the outcome of each logic is known by replacing the values of {1, −1} (neuron states) with each literal. For example, α 3,2 RANkSAT is said to be satisfiable when α 3,2 RANkSAT = 1 that provides true values. On the other hand, α 3,2 RANkSAT = −1 denotes unsatisfiable, which gives false values.…”

Section: Random K Satisfiability (Ranksat)mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

et al. 2022

View full text Add to dashboard Cite

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.

show abstract

“…Detection methods play an important and dangerous role stopping its spread in treating patients. Subsequently, many medical scientists and researchers have looked for new techniques to detect diseases more accurately and quickly [1].…”

Section: Introductionmentioning

confidence: 99%

A Review of COVID-19 Diagnosis and Detection Using Artificial Intelligence

Suhad Hussein Jasim

2022

JFES

View full text Add to dashboard Cite

Coronavirus has received widespread attention from the community of researchers and medical scientists in the past year. Deploying based on Artificial Intelligence (AI) networks and models in real world to learn about and diagnose COVID-19 is a critical mission for medical personnel to help preventing the rapid spread of this virus. This article is a brief review of recent papers concerning about detection of the virus; most of the schemes used to detect and diagnose COVID-19 rely on chest X-Ray, some on sounds of breathing, and by using electrocardiogram (ECG) trace images, all these schemes based on artificial neural network for early screening of COVID-19and estimating human mobility to limit its spread. In some studies, an accuracy rate that was obtained exceeded 95%, which is an acceptable value and that can be relied upon in the diagnosis. Therefore, currently screening tests are better in terms accuracy and reliability for diagnosing patients with severe and acute respiratory syndrome coronavirus, frequently the most used test is the (RT-PCR).

show abstract

A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network

Cited by 13 publications

References 44 publications

COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs

COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

A Review of COVID-19 Diagnosis and Detection Using Artificial Intelligence

Contact Info

Product

Resources

About