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Fulltext A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode

Salama A, Malekmohammadi A, Mohanna S, Rajkumar R

Int J Biomed Imaging, 2017;2017:3589324.
PMID: 29225613 DOI: 10.1155/2017/3589324

This paper presents a multitasking electrical impedance tomography (EIT) system designed to improve the flexibility and durability of an existing EIT system. The ability of the present EIT system to detect, locate, and reshape objects was evaluated by four different experiments. The results of the study show that the system can detect and locate an object with a diameter as small as 1.5 mm in a testing tank with a diameter of 134 mm. Moreover, the results demonstrate the ability of the current system to reconstruct an image of several dielectric object shapes. Based on the results of the experiments, the programmable EIT system can adapt the EIT system for different applications without the need to implement a new EIT system, which may help to save time and cost. The setup for all the experiments consisted of a testing tank with an attached 16-electrode array made of titanium alloy grade 2. The titanium alloy electrode was used to enhance EIT system's durability and lifespan.
Fulltext COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, et al.

Results Phys, 2021 Dec;31:105045.
PMID: 34840938 DOI: 10.1016/j.rinp.2021.105045

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.
A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application

Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA

Int J Med Inform, 2018 04;112:173-184.
PMID: 29500017 DOI: 10.1016/j.ijmedinf.2018.02.001

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.
Fulltext Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Mohammed MA, Al-Khateeb B, Yousif M, Mostafa SA, Kadry S, Abdulkareem KH, et al.

Comput Intell Neurosci, 2022;2022:1307944.
PMID: 35996653 DOI: 10.1155/2022/1307944

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.
Fulltext COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, et al.

Soft comput, 2023;27(5):2657-2672.
PMID: 33250662 DOI: 10.1007/s00500-020-05424-3

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Fulltext Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem KH, Mostafa SA, Al-Qudsy ZN, Mohammed MA, Al-Waisy AS, Kadry S, et al.

J Healthc Eng, 2022;2022:5329014.
PMID: 35368962 DOI: 10.1155/2022/5329014

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
Fulltext Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities

Mohammed MA, Abdulhasan MJ, Kumar NM, Abdulkareem KH, Mostafa SA, Maashi MS, et al.

Multimed Tools Appl, 2022 Jul 28.
PMID: 35915808 DOI: 10.1007/s11042-021-11537-0

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.
Fulltext Knowledge, Attitudes, and Practices of Adult Iraqi Population Towards COVID-19 Booster Dose: A Cross-Sectional Study

Al-Qerem W, Jarab A, Hammad A, Alsajri AH, Al-Hishma SW, Ling J, et al.

Patient Prefer Adherence, 2022;16:1525-1537.
PMID: 35769339 DOI: 10.2147/PPA.S370124

PURPOSE: COVID-19 vaccines are critical for containing the pandemic and preventing serious SARS-CoV-2 infections. In addition to the two main doses, a booster dose has been utilized to improve immunity. The aim of current study is to evaluate Iraqi adult population knowledge, attitudes, and practices towards COVID-19 booster dose.
SUBJECTS AND METHODS: This online cross-sectional survey of adult Iraqis (n = 754) assessed the attitudes of people who have had both immunizations regarding a potential COVID-19 vaccine booster dosage and to identify potential factors that might impact these attitudes. Factors evaluated in the current study included previously received vaccine type in the first two doses, socioeconomic characteristics, health status, knowledge about COVID-19 and its vaccines and adherence to protective practices.
RESULTS: Overall, 61.1% of participants expressed willingness to receive a COVID-19 booster dose, with a high median score of knowledge and practice toward COVID-19. Participants who did not perceive COVID-19 to be serious, p-value <0.001), participants who believed they would not be infected with COVID-19 in the next 6 months (p-value <0.001), low knowledge score group (p-value <0.001), lower education (p-value <0.001), participants who received the COVID-19 vaccine because of imposed laws (p-value <0.001), participants who received AstraZeneca vaccine (p-value <0.001), younger participants (p-value=0.003), low level of practice (p-value <0.001), participants who did not know someone who had died due to COVID-19 (p-value=0.01), low risk of developing serious side effects if infected with COVID-19 and participants in the low side effects score were significantly less frequently willing to receive a booster COVID-19 dose (p-value <0.001). The main reasons for booster dose hesitancy/refusal were the perceived lack of need for a booster shot, the uselessness of a booster shot and the conspiracy theory of boosting corporate profits through booster shots.
CONCLUSION: There is high hesitancy towards COVID-19 booster dose acceptance among the Iraqi population. The study identified several factors associated with vaccine hesitancy including low socioeconomic status and low knowledge about COVID-19 and its vaccines.