A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques

Hammad, Muhammed S.; Ghoneim, Vidan F.; Mabrouk, Mai S.; Al-Atabany, Walid

doi:10.1038/s41598-023-30941-0

Cited by 11 publications

(4 citation statements)

References 57 publications

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“…To create useful systems that can detect COVID-19, these signals were analyzed using digital signal processing technologies. In another study, COVID-19 was identified using genomic image-processing (GIP) methods 44 . GIP is an area of bioinformatics that connects bioinformatics with image processing techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

Antar,

Abd El-Sattar,

Abdel-Rahman

et al. 2023

Sci Rep

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The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people’s lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

Antar,

Abd El-Sattar,

Abdel-Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Hybrid models combining techniques like Sparger Wolf Hawk Optimization with deep neural networks have also shown promise for COVID-19 assessment (10) . For text data, weighting schemes have been employed to extract informative features from clinical notes for COVID-19 identification and mortality prediction (11) . Deep learning methods have effectively utilized both image and text data, proving superior to classical machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Early Differential Diagnosis of COVID-19 using Improved Fuzzy Cognitive Map Approaches

Akilashri,

Nithya

2024

IJST

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Background/Objectives: The COVID-19 pandemic has created an urgent need for rapid and accurate diagnosis to facilitate timely treatment and control spread. However, the initial symptoms can be non-specific, making early differential diagnosis challenging. Methods: This study proposes an integrated framework for COVID-19 diagnosis using improved Fuzzy Cognitive Map (FCM) approach for fast, intelligent screening of COVID-19 cases versus other respiratory conditions based on risk factors and clinical presentation. The integrated framework is designed based on combining fuzzy cognitive maps with different classifiers separately such as Random Forest (RF) classifier, Gradient Boosting (GB) classifier and Weighted Decision Tree (WDT) approach to model interrelationships between indicative symptoms and risk factors in COVID-19. The model is evaluated on a real time COVID-19 dataset of 600 patient details (i.e., 520 non-covid and 80 Covid). Due to the unbalanced nature of class labels, this work is evaluated based on balanced accuracy and Matthews’s correlation coefficient to identify the appropriate model. Findings: Among the different combination of classifiers FCM-RF attains 95.91% which outperforms 15.14%, 5.05% and 7.69% higher than normal FCM, FCM-GB and FCM-WDT. Novelty: The interpretability of the fuzzy map supplemented by the high predictive performance of random forest provides an effective decision support system for frontline healthcare workers to make quick, reliable screening decisions and ensure optimal care pathways. The integrated approach demonstrates the potential of computational intelligence in addressing critical diagnosis challenges within complex, urgent medical scenarios. Keywords: COVID-19, Ensemble Approach, Improved fuzzy cognitive maps, Balanced accuracy, Clinical dataset

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“…The most recent work on covid-19 can be found in the work of 29 , where they have determined spread of infection rate using superposition rule of Gaussian pulses and informed the world by elaborating the occurrence of multiple waves. The speed rate of this disease was high, that is why, Hammad et al 30 implemented a technique, based on image processing, for the fast detection of covid disease and its controlling. In 2021, Paul et al 31 worked on the fast emergence of covid by introducing two parameters-based model, while Photiou 32 gathered data from the social media and informed the world about the spread of this pandemic disease.…”

Section: Introductionmentioning

confidence: 99%

Generation of multi-scrolls in corona virus disease 2019 (COVID-19) chaotic system and its impact on the zero-covid policy

Marwan,

Han,

Khan

2023

Sci Rep

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In this paper, we discussed the impossibility of achieving zero-covid cases per day for all time with the help of fuzzy theory, while how a single case can trigger chaotic situation in the nearby city is elaborated using multi-scrolls. To accomplish this goal, we consider the number of new cases per day; $$x_{1}$$ x 1 to be the preferred state variable by restricting its value to the interval (0, 1). One can need to think of $$x_{1}$$ x 1 as a member of a fuzzy set and provide that set with appropriate membership functions. Moreover, how a single incident in one city can spread chaos to other cities is also addressed at length, using multi-scroll attractors and the signal excitation function. In addition, a bifurcation diagram of daily new instances vs the parameter $$\alpha _{1}$$ α 1 is shown, elaborating that daily new cases may show a decrease under strict rules and regulations, but can again lead to chaos. Apart from biologist, this paper can play vital role for engineers as well in a sense that, a signal function can be embedded in non-symmetric systems for the creation of multi-scroll attractors in all directions using a generalized algorithm that has been designed in the current work. Finally, it is our future target to show that the covid is leading towards influenza and will be no more dangerous as was in the past.

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A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques

Cited by 11 publications

References 57 publications

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

An Integrated Framework for Early Differential Diagnosis of COVID-19 using Improved Fuzzy Cognitive Map Approaches

Generation of multi-scrolls in corona virus disease 2019 (COVID-19) chaotic system and its impact on the zero-covid policy

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