RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

Basha, Sameh H.; Anter, Ahmed M.; Hassanien, Aboul Ella; Abdalla, Areeg

doi:10.1007/s00500-021-06103-7

Cited by 16 publications

(7 citation statements)

References 70 publications

(88 reference statements)

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“…Their suggested model achieved an accuracy of 96% for two-way classification. Basha et al (2021) [ 33 ] reported a neurotrophic model for COVID-19 diagnosis from chest X-ray images with an accuracy of 98.7% for two-way classification.…”

Section: Resultsmentioning

confidence: 99%

A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images

Al-Shourbaji

Kachare²,

Abualigah

et al. 2022

Pathogens

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Pre-trained machine learning models have recently been widely used to detect COVID-19 automatically from X-ray images. Although these models can selectively retrain their layers for the desired task, the output remains biased due to the massive number of pre-trained weights and parameters. This paper proposes a novel batch normalized convolutional neural network (BNCNN) model to identify COVID-19 cases from chest X-ray images in binary and multi-class frameworks with a dual aim to extract salient features that improve model performance over pre-trained image analysis networks while reducing computational complexity. The BNCNN model has three phases: Data pre-processing to normalize and resize X-ray images, Feature extraction to generate feature maps, and Classification to predict labels based on the feature maps. Feature extraction uses four repetitions of a block comprising a convolution layer to learn suitable kernel weights for the features map, a batch normalization layer to solve the internal covariance shift of feature maps, and a max-pooling layer to find the highest-level patterns by increasing the convolution span. The classifier section uses two repetitions of a block comprising a dense layer to learn complex feature maps, a batch normalization layer to standardize internal feature maps, and a dropout layer to avoid overfitting while aiding the model generalization. Comparative analysis shows that when applied to an open-access dataset, the proposed BNCNN model performs better than four other comparative pre-trained models for three-way and two-way class datasets. Moreover, the BNCNN requires fewer parameters than the pre-trained models, suggesting better deployment suitability on low-resource devices.

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Section: Resultsmentioning

confidence: 99%

A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images

Al-Shourbaji

Kachare²,

Abualigah

et al. 2022

Pathogens

Self Cite

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“…Therefore, the lack of a dataset has driven researchers to do more exploration on the dataset. Therefore, such a Generative Adversarial Network (GAN) technique is needed to overcome the dataset's limitation [6][7][8]. In recent research in the Journal of Radiology, X-ray chest imagery is superior to outclassed lab testing such as PCR or rapid tests.…”

Section: Related Workmentioning

confidence: 99%

Disease-Aware Chest X-Ray Style GAN Image Generation and CatBoost Gradient Boosted Trees

Mansur

2024

IJACSA

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Intelligence has significantly advanced and is proficient in image classification. Even though the COVID-19 pandemic has ended, the virus is now considered to have entered an endemic phase. Historically, COVID-19 detection has predominantly depended on a single technology known as the polymerase chain reaction (PCR). The academic community is keen radiograph data to forecast COVID-19 because of its prospective advantages. The proposed methodology aims to improve dataset quality by utilizing artificially generated images produced by StyleGAN. The ratio of 59:41 was used to combine the synthetic datasets with the real ones. The combination of the StyleGAN framework, the VGG19, and CatBoost Gradient Boosted Trees is to improve prediction accuracy. Accurate and precise measurements significantly impact the evaluation of a model's performance. The assessment resulted in 98.67% accurate and 97.21% precise. In the future, we may enhance the diversity and quality of the collection by integrating other datasets from different sources with the Chest X-ray dataset.

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“…Generally, for pulling out useful information, edge detection is useful which gives us the detailed information of the object. In the proposed model, Sobel Edge Detection algorithm 46 , 47 , 48 is used which is made up of 3*3 convolutional kernels.…”

Section: Proposed Modelmentioning

confidence: 99%

A deep learning approach for classification of COVID and pneumonia using DenseNet‐201

Sanghvi

Patel

Agarwal

et al. 2022

Int J Imaging Syst Tech

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In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X‐ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X‐ray Image. The software then uploads chest X‐ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio‐Medical Innovation is a user‐ready framework which assists the medical providers in providing the patients with the best‐suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

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RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

Cited by 16 publications

References 70 publications

A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images

A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images

Disease-Aware Chest X-Ray Style GAN Image Generation and CatBoost Gradient Boosted Trees

A deep learning approach for classification of COVID and pneumonia using DenseNet‐201

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