Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network: A Survey

This paper offers a review of different types of Error Correction Scheme (ECS) used in communication systems in general, which is followed by a summary of the IEEE standard for Wireless Body Area Network (WBAN). The possible types of channels and network models for WBAN are presented that are crucial to the design and implementation of ECS. Following that, a literature review on the proposed ECSs for WBAN is conducted based on different aspects. One aspect of the review is to examine what type of parameters are considered during the research work. The second aspect of the review is to analyse how the reliability is measured and whether the research works consider the different types of reliability and delay requirement for different data types or not. The review indicates that the current literatures do not utilize the constraints that are faced by WBAN nodes during ECS design. Subsequently, we put forward future research challenges and opportunities on ECS design and the implementation for WBAN when considering computational complexity and the energy-constrained nature of nodes

A Radar-Enabled Collaborative Sensor Networking Integrating COTS Technology for Surveillance and Tracking

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (\u3c$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost ( < $50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients. 2022 by the authors.This work was supported by the Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), and UKM Grant Number DIP-2020-004, Grant Number GP-2020-K017701, and by the Qatar National Research fund under Grant UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (5% and 50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.This work was supported by Qatar National Research Fund (QNRF) under Grant UREP28-144-3-046 and Qatar University Emergency Response Grant (QUERG-CENG-2020-1) through Qatar University. Open Access publication is funded by Qatar National Library (QNL).Scopu

Interactive Effects of Arbuscular Mycorrhizal Inoculation with Nano Boron, Zinc, and Molybdenum Fertilization on Stevioside Contents of Stevia (Stevia rebaudiana, L.) Plants

Stevia (Stevia rebaudiana, L.) is receiving increasing global interest as a diabetes-focused herb associated with zero-calorie stevioside sweetener glycoside production. This study was conducted to determine whether the arbuscular mycorrhiza (AM), as a biofertilizer integrated with nano boron (B), zinc (Zn), and molybdenum (Mo), would improve stevia growth and stevioside content. A factorial experiment with four replicates was conducted to evaluate the effect of AM at 0, 150, and 300 spore/g soil and three nano microelements B at 100 mg/L, Zn at 100 mg/L, and Mo at 40 mg/L on growth performance, stevioside, mineral contents, and biochemical contents of stevia. Results indicated that the combination of AM at 150 and B at 100 mg/L significantly increased plant height, number of leaves, fresh and dry-stem, and herbal g/plant during the 2019 and 2020 growing seasons. Chlorophyll content was increased by the combination between AM at 150 spore/g soil and B at 100 mg/L during both seasons. Stevioside content in leaves was increased by AM at 150 spore/g soil and B at 100 mg/L during the second season. In addition, N, P, K, Zn, and B in the leaf were increased by applying the combination of AM and nano microelements. Leaf bio constituent contents were increased with AM at 150 spore/g soil and B at 100 mg/L during both seasons. The application of AM and nano B can be exploited for high growth, mineral, and stevioside contents as a low-calorie sweetener product in stevia