RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Manogaran, Gunasekaran; Loey, Mohamed

doi:10.1007/s11051-020-05041-z

Cited by 19 publications

(13 citation statements)

References 37 publications

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“…Khalifa et al [ 40 ] developed a classification approach for the treatment purposes of coronavirus on a single human cell-based on treatment type and treatment concentration level using deep learning and machine learning (ML) methods. Numerical features of the data sets were converted to images for building the DCNN model.…”

Section: Related Literaturementioning

confidence: 99%

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

Alam

Ahsan

Based

et al. 2021

Sensors

145

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Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

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Section: Related Literaturementioning

confidence: 99%

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

Alam

Ahsan

Based

et al. 2021

Sensors

145

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“…The conventional machine learning (ensemble) algorithm attained 98.5 percent in study precision in the calculation of treatment concentration level, whereas the projected DCNN method attained 98.2 percent. The performance metrics affirm the accuracy of the outcomes obtained from the research carried out in the classification of therapy and the prediction level of treatment focus [ 32 ]. Utilizing traditional and ensemble machine learning algorithms, it has been suggested to divide documented medical reports into 4 categories.…”

Section: Literature Reviewsmentioning

confidence: 80%

“…In order to show the probability of machine learning for impending investigation, the article provides an initial benchmarking [ 36 ]. In author [ 32 ], they concentrate on current advances in the expansion of artificial intelligence-based COVID-19 medications and inoculations and the ability for smart training to determine COVID-19 infected individual. To promote deep learning applications for SARS-COV-2, several molecular targets of COVID-19, whose inhibition can improve patient survival, are illuminated.…”

Section: Literature Reviewsmentioning

confidence: 99%

Machine learning-based IoT system for COVID-19 epidemics

et al. 2022

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The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.

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“…The drugs identified in this research are similar to the drugs identified by Recursion in their study [5] and by the study reported in [17], as all resulted in GS-441524 and Remdesivir being isolated as clear outliers in our probability scores and also their efficacy scores. Notably, our CX-4945 probability score is much more noticeable for the same compound, although all results share the same four most effective drugs, albeit in different rankings and orders of magnitude [28]. Of the identified compounds, CX-4945 and Aloxistatin have entered the observational clinical pretrial phase, while Remdesivir (and by association GS-441524) has been approved by the FDA as the first treatment for COVID-19.…”

Section: Experimental Studymentioning

confidence: 81%

Deep Learning for Drug Discovery: A Study of Identifying High Efficacy Drug Compounds Using a Cascade Transfer Learning Approach

Zhuang

Ibrahim

2021

Applied Sciences

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In this research, we applied deep learning to rank the effectiveness of candidate drug compounds in combating viral cells, in particular, SARS-Cov-2 viral cells. For this purpose, two different datasets from Recursion Pharmaceuticals, a siRNA image dataset (RxRx1), which were used to build and calibrate our model for feature extraction, and a SARS-CoV-2 dataset (RxRx19a) was used to train our model for ranking efficacy of candidate drug compounds. The SARS-CoV-2 dataset contained healthy, uninfected control or “mock” cells, as well as “active viral” cells (cells infected with COVID-19), which were the two cell types used to train our deep learning model. In addition, it contains viral cells treated with different drug compounds, which were the cells not used to train but test our model. We devised a new cascade transfer learning strategy to construct our model. We first trained a deep learning model, the DenseNet, with the siRNA set, a dataset with characteristics similar to the SARS-CoV-2 dataset, for feature extraction. We then added additional layers, including a SoftMax layer as an output layer, and retrained the model with active viral cells and mock cells from the SARS-CoV-2 dataset. In the test phase, the SoftMax layer outputs probability (equivalently, efficacy) scores which allows us to rank candidate compounds, and to study the performance of each candidate compound statistically. With this approach, we identified several compounds with high efficacy scores which are promising for the therapeutic treatment of COVID-19. The compounds showing the most promise were GS-441524 and then Remdesivir, which overlapped with these reported in the literature and with these drugs that are approved by FDA, or going through clinical trials and preclinical trials. This study shows the potential of deep learning in its ability to identify promising compounds to aid rapid responses to future pandemic outbreaks.

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RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

Cited by 19 publications

References 37 publications

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

Machine learning-based IoT system for COVID-19 epidemics

Deep Learning for Drug Discovery: A Study of Identifying High Efficacy Drug Compounds Using a Cascade Transfer Learning Approach

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