Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches
COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
Deep learning with radiomics for disease diagnosis and treatment: challenges and potential
The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …
Quantitative evaluation of EEG-biomarkers for prediction of sleep stages
Electroencephalography (EEG) is immediate and sensitive to neurological changes
resulting from sleep stages and is considered a computing tool for understanding the …
resulting from sleep stages and is considered a computing tool for understanding the …
Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma
Glioma represents a dominant primary intracranial malignancy in the central nervous
system. Artificial intelligence that mainly includes machine learning, and deep learning …
system. Artificial intelligence that mainly includes machine learning, and deep learning …
Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
[HTML][HTML] Introduction to radiomics for a clinical audience
C McCague, S Ramlee, M Reinius, I Selby, D Hulse… - Clinical Radiology, 2023 - Elsevier
Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …
features from medical images, thus converting these digital images into minable, high …
Molecular subtype classification of low‐grade gliomas using magnetic resonance imaging‐based radiomics and machine learning
In 2016, the World Health Organization (WHO) updated the glioma classification by
incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new …
incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new …
[HTML][HTML] Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection
E Avard, I Shiri, G Hajianfar, H Abdollahi… - Computers in Biology …, 2022 - Elsevier
Objective Robust differentiation between infarcted and normal tissue is important for clinical
diagnosis and precision medicine. The aim of this work is to investigate the radiomic …
diagnosis and precision medicine. The aim of this work is to investigate the radiomic …
RADIC: A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics
O Attallah - Chemometrics and Intelligent Laboratory Systems, 2023 - Elsevier
Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and
accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The …
accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The …
Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
W Kang, X Qiu, Y Luo, J Luo, Y Liu, J Xi, X Li… - Journal of Translational …, 2023 - Springer
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …