A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset.

Fenofibrate and statins in patients with COVID-19

Statins, which are widely used to treat hypercholesterolemia, have anti-inflammatory and antioxidant effects, upregulate angiotensin-converting enzyme 2 (ACE2) receptors, which happen to be SARS-CoV-2's gateway into cells. This study aims to analyse the effects of Fenofibrate in comparison to Statins and a control group in patients with COVID-19. This is a retrospective open blind observational study of cohort of 300 patients experienced COVID-19 (symptoms' severity varied between patients). The participants were divided into three cohorts;a control group received standard COVID-19 treatment (n=100);a second group (n=100) of patients who were on Statins, in addition they received the standard treatment;and a third cohort for patients who were already taking Fenofibrate (TRICORR) as a medication to treat hyperlipidemia (n=100). Most symptoms (including cough, exertional dyspnoea, SOB, sore throat, sneezing, headache, tiredness, agitation, diarrhoea, joint pain, insomnia, myalgia, and fatigue) were less prevalent for patients who administered antihyperlipidemic drugs compared to the control group. Patients who were already taking Cholesterol-lowering medication presented with symptoms varied between mild to severe. Patients on Statins or Fenofibrate also showed less tachycardia and tachypnoea compared to those who were not on antihyperlipidemic drugs, and also the need for oxygen and ICU admission were less frequent. The length of stay in hospital was shorter in patients who were already on Statins or Fenofibrate. Both Statins and Fenofibrate have improved the outcome and the severity of symptoms for patients with Covid 19 infection.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Fenofibrate and Statins in Patients with COVID-19

Statins, which are widely used to treat hypercholesterolemia, have anti-inflammatory and antioxidant effects, upregulate angiotensin-converting enzyme 2 (ACE2) receptors, which happen to be SARS-CoV-2's gateway into cells. This study aims to analyse the effects of Fenofibrate in comparison to Statins and a control group in patients with COVID-19. This is a retrospective open blind observational study of cohort of 300 patients experienced COVID-19 (symptoms' severity varied between patients). The participants were divided into three cohorts;a control group received standard COVID-19 treatment (n=100);a second group (n=100) of patients who were on Statins, in addition they received the standard treatment;and a third cohort for patients who were already taking Fenofibrate (TRICOR) as a medication to treat hyperlipidemia (n=100). Most symptoms (including cough, exertional dyspnoea, SOB, sore throat, sneezing, headache, tiredness, agitation, diarrhoea, joint pain, insomnia, myalgia, and fatigue) were less prevalent for patients who administered antihyperlipidemic drugs compared to the control group. Patients who were already taking Cholesterol-lowering medication presented with symptoms varied between mild to severe. Patients on Statins or Fenofibrate also showed less tachycardia and tachypnoea compared to those who were not on antihyperlipidemic drugs, and also the need for oxygen and ICU admission were less frequent. The length of stay in hospital was shorter in patients who were already on Statins or Fenofibrate. Both Statins and Fenofibrate have improved the outcome and the severity of symptoms for patients with Covid 19 infection. Copyright © RJPT All right reserved.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l’Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.