Medicine and Health Sciences | Open Access Articles

Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel

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Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and …

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Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz

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In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …

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Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz

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Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on …

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Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah

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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can …

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Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

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COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

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Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali

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This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. …

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Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz

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This study develops an atlas-based automated framework for segmenting infants' brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant's brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix …

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A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir

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A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in …

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A Novel Text Mining Approach For Mental Health Prediction Using Bi-Lstm And Bert Model, Kamil Zeberga, Muhammad Attique, Babar Shah, Farman Ali, Yalew Zelalem Jembre, Tae-Sun Chung

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With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify …

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Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura

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Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In …

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Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni

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The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …

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Full-Text Articles in Medicine and Health Sciences

Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel

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Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz

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Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz

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Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah

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Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

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Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali

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Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz

All Works

A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir

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A Novel Text Mining Approach For Mental Health Prediction Using Bi-Lstm And Bert Model, Kamil Zeberga, Muhammad Attique, Babar Shah, Farman Ali, Yalew Zelalem Jembre, Tae-Sun Chung

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Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura

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Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni

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