Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach.
Mohammad MehrpouyanHamed ZamanianGhazal Mehri-KakavandMohamad PursamimiAhmad ShalbafMahdi GhorbaniAmirhossein Abbaskhani DavanlooPublished in: Physical and engineering sciences in medicine (2022)
The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.
Keyphrases
- computed tomography
- end stage renal disease
- coronavirus disease
- contrast enhanced
- sars cov
- deep learning
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- machine learning
- healthcare
- multiple sclerosis
- peritoneal dialysis
- positron emission tomography
- magnetic resonance imaging
- image quality
- squamous cell carcinoma
- climate change
- palliative care
- patient reported outcomes
- magnetic resonance
- convolutional neural network
- pulmonary embolism
- patient reported
- quality improvement
- respiratory syndrome coronavirus
- neural network
- affordable care act