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Article ; Online: Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

Albahli, Saleh

2022 Volume 10, Page(s) 966779

Abstract: The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as ... ...

Abstract	The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Pandemics ; Social Media ; Data Mining ; Attitude
Language	English
Publishing date	2022-10-10
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.966779
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia.

Albahli, Saleh

International journal of medical sciences

2020 Volume 17, Issue 10, Page(s) 1439–1448

Abstract: ... ...

Abstract	Background
MeSH term(s)	COVID-19 ; Coronavirus Infections/complications ; Coronavirus Infections/diagnostic imaging ; Deep Learning ; Female ; Humans ; Male ; Pandemics ; Pneumonia, Viral/complications ; Pneumonia, Viral/diagnostic imaging ; Radiography, Thoracic
Keywords	covid19
Language	English
Publishing date	2020-06-06
Publishing country	Australia
Document type	Journal Article ; Validation Study
ZDB-ID	2151424-0
ISSN	1449-1907 ; 1449-1907
ISSN (online)	1449-1907
ISSN	1449-1907
DOI	10.7150/ijms.46684
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

Albahli, Saleh

Current medical imaging

2020 Volume 17, Issue 1, Page(s) 109–119

Abstract: Background: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at ... ...

Abstract	Background: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. Methods: Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. Conclusion: The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation.
MeSH term(s)	Adolescent ; Adult ; Aged ; COVID-19/diagnostic imaging ; Child ; Child, Preschool ; Diagnosis, Differential ; Female ; Humans ; Infant ; Male ; Middle Aged ; Neural Networks, Computer ; Radiography, Thoracic/methods ; Thoracic Diseases/diagnostic imaging ; Young Adult
Keywords	covid19
Language	English
Publishing date	2020-06-04
Publishing country	United Arab Emirates
Document type	Journal Article
ISSN	1573-4056
ISSN (online)	1573-4056
DOI	10.2174/1573405616666200604163954
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images.

Albahli, Saleh / Nazir, Tahira

Bioengineering (Basel, Switzerland)

2023 Volume 10, Issue 10

Abstract: Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the ... ...

Abstract	Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.
Language	English
Publishing date	2023-09-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering10101147
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Article ; Online: A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION

Saleh ALBAHLI

Applied Computer Science, Vol 16, Iss 3, Pp 5-

2020 Volume 15

Abstract: Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model ... ...

Abstract	Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.
Keywords	deep neural network ; unlabeled dataset ; just-in-time defect prediction ; unsupervised prediction ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	Polish Association for Knowledge Promotion
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification.

Albahli, Saleh / Masood, Momina

Frontiers in plant science

2022 Volume 13, Page(s) 1003152

Abstract: Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the ... ...

Abstract	Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.
Language	English
Publishing date	2022-10-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.1003152
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification.

Albahli, Saleh / Nawaz, Marriam

Frontiers in plant science

2022 Volume 13, Page(s) 957961

Abstract: Early recognition of tomato plant leaf diseases is mandatory to improve the food yield and save agriculturalists from costly spray procedures. The correct and timely identification of several tomato plant leaf diseases is a complicated task as the ... ...

Abstract	Early recognition of tomato plant leaf diseases is mandatory to improve the food yield and save agriculturalists from costly spray procedures. The correct and timely identification of several tomato plant leaf diseases is a complicated task as the healthy and affected areas of plant leaves are highly similar. Moreover, the incidence of light variation, color, and brightness changes, and the occurrence of blurring and noise on the images further increase the complexity of the detection process. In this article, we have presented a robust approach for tackling the existing issues of tomato plant leaf disease detection and classification by using deep learning. We have proposed a novel approach, namely the DenseNet-77-based CornerNet model, for the localization and classification of the tomato plant leaf abnormalities. Specifically, we have used the DenseNet-77 as the backbone network of the CornerNet. This assists in the computing of the more nominative set of image features from the suspected samples that are later categorized into 10 classes by the one-stage detector of the CornerNet model. We have evaluated the proposed solution on a standard dataset, named PlantVillage, which is challenging in nature as it contains samples with immense brightness alterations, color variations, and leaf images with different dimensions and shapes. We have attained an average accuracy of 99.98% over the employed dataset. We have conducted several experiments to assure the effectiveness of our approach for the timely recognition of the tomato plant leaf diseases that can assist the agriculturalist to replace the manual systems.
Language	English
Publishing date	2022-09-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.957961
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.

Albahli, Saleh / Nazir, Tahira

Frontiers in medicine

2022 Volume 9, Page(s) 955765

Abstract: Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging ... ...

Abstract	Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.
Language	English
Publishing date	2022-08-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2775999-4
ISSN	2296-858X
ISSN	2296-858X
DOI	10.3389/fmed.2022.955765
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia

Albahli, Saleh

Int J Med Sci

Abstract: Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health ... ...

Abstract	Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results. Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #633909
Database	COVID19

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Article: A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases using X-ray Images

Albahli, Saleh

Abstract: BACKGROUND: Scanning patient's lungs to detect a Coronavirus 2019 (COVID-19) may lead to similar imaging with other chest diseases that strongly requires a multidisciplinary approach to confirm the diagnosis. There are only few works targeted ... ...

Abstract	BACKGROUND: Scanning patient's lungs to detect a Coronavirus 2019 (COVID-19) may lead to similar imaging with other chest diseases that strongly requires a multidisciplinary approach to confirm the diagnosis. There are only few works targeted pathological x-ray images. Most of the works targeted only single disease detection which is not good enough. Some works have provided for all classes however the results suffer due to lack of data for rare classes and data unbalancing problem. METHODS: Due to arise of COVID-19 virus medical facilities of many countries are overwhelmed and there is a need of intelligent system to detect it. There have been few works regarding detection of the coronavirus but there are many cases where it can be misclassified as some techniques do not provide any goodness if it can only identify type of diseases and ignore the rest. This work is a deep learning-based model to distinguish between cases of COVID-19 from other chest diseases which is need of today. RESULTS: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provide effective analysis of chest related diseases with respect to age and gender. Our model achieves 87% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. CONCLUSION: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #526800
Database	COVID19

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