A deep learning semantic segmentation architecture for COVID-19 lesions discovery in limited chest CT datasets.

During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID-19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre-processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics.

A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic.

The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection.

Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel approach for estimating the smart medical devices (SMDs) selection process in a group decision making (GDM) in a vague decision environment. The complexity of the selected decision criteria for the smart medical devices is a significant feature of this analysis. To simulate these processes, a methodology that combines neutrosophics using bipolar numbers with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under GDM is suggested. Neutrosophics with TOPSIS approach is applied in the decision making process to deal with the vagueness, incomplete data and the uncertainty, considering the decisions criteria in the data collected by the decision makers (DMs). In this research, the stress is placed upon the choosing of sugar analyzing smart medical devices for diabetics' patients. The main objective is to present the complications of the problem, raising interest among specialists in the healthcare industry and assessing smart medical devices under different evaluation criteria. The problem is formulated as a multi criteria decision type with seven alternatives and seven criteria, and then edited as a multi criteria decision model with seven alternatives and seven criteria. The results of the neutrosophics with TOPSIS model are analyzed, showing that the competence of the acquired results and the rankings are sufficiently stable. The results of the suggested method are also compared with the neutrosophic extensions AHP and MOORA models in order to validate and prove the acquired results. In addition, we used the SPSS program to check the stability of the variations in the rankings by the Spearman coefficient of correlation. The selection methodology is applied on a numerical case, to prove the validity of the suggested approach.

Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals.

In this paper, MODWT is used to decompose the Electrocardiography (ECG) signals and to identify the changes of R waves in the noisy input ECG signal. The MODWT is used to handle the arbitrary changes in the input signal. The R wave's detctected by the proposed framework is used by the doctors and careholders to take necessary action for the patients. MATLAB simulink model is used to develop the simulation model for the MODWT method. The performance of the MODWT based remote health monitoring system method is comparatively analyzed with other ECG monitoring approaches such as Haar Wavelet Transformation (HWT) and Discrete Wavelet Transform (DWT). Sensitivity, specificity, and Receiver Operating Characteristic (ROC) curve are calculated to evaluate the proposed Internet of Things with MODWT based ECG monitoring system. We have used MIT-BIH Arrythmia Database to perform the experiments.

Redemptive Resource Sharing and Allocation Scheme for Internet of Things-Assisted Smart Healthcare Systems.

Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems.

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

CDP-UA: Cognitive Data Processing Method Wearable Sensor Data Uncertainty Analysis in the Internet of Things Assisted Smart Medical Healthcare Systems.

Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; predicting and monitoring end-user health using physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) to improve WS data management's efficiency. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve WS data delivery in the clinical/medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset.

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90-10%, 80-20%, 70-30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection.

Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the finding of an object in images. The objective of this paper is to annotate and localize the medical face mask objects in real-life images. Wearing a medical face mask in public areas, protect people from COVID-19 transmission among them. The proposed model consists of two components. The first component is designed for the feature extraction process based on the ResNet-50 deep transfer learning model. While the second component is designed for the detection of medical face masks based on YOLO v2. Two medical face masks datasets have been combined in one dataset to be investigated through this research. To improve the object detection process, mean IoU has been used to estimate the best number of anchor boxes. The achieved results concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector. Finally, a comparative result with related work has been presented at the end of the research. The proposed detector achieved higher accuracy and precision than the related work.

Retraction Note: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.

[This retracts the article DOI: 10.1007/s11051-020-05041-z.].

A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.

The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with Conditional Generative Adversarial Nets (CGAN) based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for COVID-19 especially in chest CT images are the main motivation of this research. The main idea is to collect all the possible images for COVID-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the Coronavirus-infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%, sensitivity 77.66%, and specificity of 87.62%.

A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.