An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Masadeh, Mahmoud; Masadeh, A.; AlShorman, Omar; Khasawneh, Falak H; Masadeh, Mahmoud Ali

doi:10.11591/ijai.v11.i1.pp356-366

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…ML is integrated into various areas, including healthcare [13], hardware design [14] [15], quality control [16], and NLP [12], with this study focusing on the latter. Information is a systematic collection of discrete facts that suppresses the complete range of typical patterns.…”

Section: B Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Masadeh,

et al. 2024

IJACSA

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Arabic Text Classification (ATC) is a crucial step for various Natural Language Processing (NLP) applications. It emerged as a response to the exponential growth of online content like social posts and review comments. In this study, preprocessing techniques and representation models are used to evaluate the effectiveness of ATC using Machine Learning (ML). Generally, the ATC operation depends on various factors, such as stemming in preprocessing, feature extraction and selection, and the nature of the dataset. To enhance the overall classification performance, preprocessing methodologies are primarily employed to transform each Arabic term into its root form and reduce the dimensionality of representation. In the representation of Arabic text, feature extraction and selection processes are imperative, as they significantly enhance the performance of ATC. This study implements the chosen classifiers using various feature selection algorithms. The comprehensive assessment of classification outcomes is conducted by comparing various classifiers, including Multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and linear Support Vector Classifier (LSVC). These ML classifiers are assessed utilizing short and long Arabic text benchmark datasets called BBC Arabic corpus and the COVID-19 dataset. The assessment findings indicate that the efficacy of classification is significantly influenced by the preprocessing methods, representation model, classification algorithm, and the datasets' characteristics. In most cases, the SGDC and LSVC have consistently surpassed other classifiers for the datasets under consideration when significant features are chosen.

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Section: B Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Masadeh,

et al. 2024

IJACSA

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“…Each filter scans the input using a sliding window, performing element-wise multiplication and summation. [15] This allows the network to detect edges, textures, and more complex features at different spatial locations. CNNs learn hierarchical data representations using convolutional layers.…”

Section: Convolution Neural Network Modelmentioning

confidence: 99%

Classification of Chest X-ray Images using CNN for Medical Decision Support System

Garud,

Patel,

Kadlag

et al. 2023

IJRITCC

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X-rays are a crucial tool used by healthcare professionals to diagnose a range of medical conditions. However, it is important to keep in mind that a timely and accurate diagnosis is crucial for effective patient management and treatment. While chest X-rays can provide highly precise anatomical data, manual interpretation of the images can be time-consuming and prone to errors, which can lead to delays or incorrect diagnoses. To address these issues, healthcare systems have taken steps to improve diagnostic imaging services following the impact of the COVID-19 pandemic. While deep learning-based automated systems for classifying chest X-rays have shown promise, there are still several challenges that need to be addressed before they can be widely used in clinical settings, including the lack of comprehensive and high-quality datasets. To overcome these limitations, a real-time DICOM dataset, has been converted to JPEG format to increase processing speed and improve data control. Three pre-trained models and a convolutional neural network (CNN) model with low complexity and three convolutional layers for feature extraction, along with max pooling layers and ReLU and Softmax activation functions have been implemented. With an validation accuracy of 95.05% on their CNN model using the SGD optimizer, the result has been validated using a separate, real-time unlabeled DICOM dataset of 1000 X-ray images.

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“…ML is used in various applications, e.g., healthcare [16], hardware design [17], quality control [18], and NLP, where this work targets NLP application. Information is an organised collection of discrete pieces of data, and it conceals the whole spectrum of representational patterns.…”

Section: B Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Masadeh¹,

Davanager²,

Muaad³

2022

IJACSA

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Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.

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An efficient machine learning-based COVID-19 identification utilizing chest X-ray images

Cited by 6 publications

References 40 publications

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Classification of Chest X-ray Images using CNN for Medical Decision Support System

A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

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