access icon free Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time-consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X-ray images of normal, pneumonia and coronavirus disease-2019 (COVID-19) patients and process them to boost the COVID-specific features (opacities etc.), which enable to perform sensitive identification of COVID-19 patients. The sets of original and processed images are used with a stack of pre-trained deep models for ensemble learning. They used VGG-16 as base-learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base-learner predictions. The proposed fusion-based model is trained and tested for three types of classification, TYPE-I: binary (NORMAL/ABNORMAL), TYPE-II: binary (PNEUMONIA/COVID-19) and TYPE-III: multi-class (NORMAL/PNEUMONIA/COVID-19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID-19 patients and thereby mitigating the effect of the virus.

Inspec keywords: diagnostic radiography; patient diagnosis; diseases; medical image processing; learning (artificial intelligence); image fusion; regression analysis

Other keywords: base-learners; chest X-ray images; COVID-specific features; current testing rate; sensitivity values; COVID-19 patients; novel coronavirus; available testing methodologies; fusion-based model; logistic regression model; meta learner; coronavirus disease; ensemble learning-based COVID-19 detection; sensitive automated diagnosis; pre-trained deep models; exponential rate; base-learner predictions

Subjects: Sensor fusion; Regression analysis; Machine learning (artificial intelligence); X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Regression analysis; Probability theory, stochastic processes, and statistics; X-rays and particle beams (medical uses); Biology and medical computing; Patient diagnostic methods and instrumentation; Optical, image and video signal processing; Computer vision and image processing techniques

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