Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray

Khadhim, Ban Jawad; Kadhim, Qusay Kanaan; Shams, Wafaa Khazaal; Ahmed, Shaymaa Taha; Alsiadi, Wasan A. Wahab

doi:10.11591/ijeecs.v29.i2.pp852-860

Cited by 7 publications

(7 citation statements)

References 33 publications

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“…The microarray usually is used for representing the genetic datasets. In other word, the contents of such datasets are thousands of genes, which not all of them are important or relevant to the classification process [12]. The size of the dataset and the nature of the genes as well effect on the performance of the classification models -i.e., the classification accuracy, and on the time of learning process.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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Alzheimer's Disease (AD), which is left untreated, is the primary cause of neurodegenerative dementia, which primarily affects those over 65. Memory and thought processes are gradually disrupted by the irreversible brain disruption and, eventually, the capacity to fulfil simple tasks. Detecting AD early prevents its progression and diminishes its signs. The study aimed to build a classification model that might predict the early stages of Alzheimer's disease using accurate early-stage gene expression data from blood obtained from a clinical Alzheimer's dataset. The datasets used in this work were gathered from the Gene Expression Omnibus (GEO) and are GSE63060 and GSE63061.It has the right rows for (569 samples) and columns for (16382 genes). The suggested GWO gene selection method aims to identify the ideal feature subset for medical data. A model for predicting early Alzheimer's disease is proposed based on the modified Grey Wolf Optimizer and Support Vector Machine (GWO-SVM). These methods will help reduce the number of trivial and redundant genes in the original datasets. We attained an accuracy of 82%-88% using only the Support Vector Machine method. When we utilized several evolutionary algorithms to implement gene selection, we observed an increase in accuracy of 6%-11%, with modified Grey Wolf optimization with Crossover (CGWO) called the proposed algorithm (CGWO-SVM) achieving the highest accuracy of (97.41%) with the Alzheimer's disease dataset in comparison to the other competitive schemes in the existing literature. Thus, the proposed system is suitable for picking more accurate classification and interesting genes to increase classification accuracy while decreasing gene dimensions.

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Section: Methodsmentioning

confidence: 99%

“…Precision = TP/(TP+FP) (11) Recall: The total number of true positives multiplied by the sum of the false negatives and true positives in the following Eq. (12).…”

Section: Evaluation Measuresmentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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show abstract

“…The support vector machine (SVM) is a ground-breaking machine learning algorithm for classification and regression purposes and is quickly supplanting neural network it is really a super arrangement of neural network algorithms as the instrument of decision for nonlinear prediction, estimation and pattern recognition system, fundamentally because of their capacity to sum up well on new information and their strong hypothetical establishment [26]. The SVM regression includes a nonlinear mapping of a n-dimensional information space into a high dimensional component space [27]. A direct relapse is then performed in this elemental space.…”

Section: Support Vector Machine Regressionmentioning

confidence: 99%

Using support vector machine regression to reduce cloud security risks in developing countries

Dhahi

Khadhim

et al. 2023

IJEECS

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The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

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“…Machine learning (ML) has been a potent tool in healthcare in recent years. Examples include breast cancer recognition [3], detection of lung cancer [4], parkinson disease classification [5], diagnosis of hepatitis disease [6], prediction of cardiac illness [7], chronic and infectious diseases [8], the severity grading and identifying of diabetic retinopathy [9], [10], and the prediction of infected COVID-19 [11]- [14]. The accuracy of diagnosis can be significantly increased by using ML in the early identification of CKD.…”

Section: Introductionmentioning

confidence: 99%

Detection of chronic kidney disease using binary whale optimization algorithm

Sutikno,

Kusumaningrum,

Sugiharto

et al. 2024

IJ-AI

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Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

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Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray

Cited by 7 publications

References 33 publications

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Using support vector machine regression to reduce cloud security risks in developing countries

Detection of chronic kidney disease using binary whale optimization algorithm

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