Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season.
 • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets.
 • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

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A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).

In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

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A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

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A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

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A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

This paper presents a complete approach for automatic detection and classification of pulmonary nodules through applying several techniques on chest CT images. The proposed approach starts by multi segmentation techniques followed by nodule extraction and then classification of the extracted features. The first step of is the preprocessing by converting the pixel values into Hounsfield units, and then applying a region growing technique to isolate the human body from other items. After that, the lung segmentation process takes place, which initially starts by thresholding the image followed by Hessian method for vascular tree segmentation. The application of multi segmentation methods allowed the precise extraction of the nodule features, which facilitate the classification. After this stage, several classifiers with their combination had been applied to perform the classification of malignant nodule and benign. The combination of these classifiers are used to evaluate and then enhance the classification accuracy of malignant nodule and benign. CT study of 200 patients, each with 150 to 500 images, is used to evaluate the proposed technique. The results are promising and achieved 98% classification accuracy.

Automatic detection of the pulmonary nodules from CT images

A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter

Although the inertial navigation system (INS) can provide a continuous navigation solution for most of unmanned surface vehicles (USVs), its reliability decreases over time due to the accelerometer and gyroscope drifts. The authors’ study introduces a novel method based on integrated micro electric mechanical system (MEMS)-INS smartphone sensors with global positioning system (GPS) and Doppler velocity log (DVL). Since the GPS and DVL are used as reference systems to correct MEMS-INS errors, their accuracies are decreased due to the bad weather and the noise of DVL, which effect on the efficiency of a whole navigation system. To resolve this problem, the adaptive data sharing factor combined filter (DSFCF) is used as integrated method. According to adaptive DSFCF integrated method, the reference system which has the beast accuracy is used to correct the USV navigation errors, and at the same time the least accuracy system will be detected and avoided. The proposed GPS/DVL/MEMS-INS using adaptive DSFCF integrated method was tested on a surface reference trajectory in Mukalla city-Yemen. The estimated results by this proposed method could provide a reliable navigation solution and give the least error compared by the GPS/DVL/MEMS-INS Centralised Kalman filter and the GPS/DVL/MEMS-INS constant DSFCF integrated methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2019.0015

GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance USV navigation system based on adaptive DSFCF

Breast cancer is a significant health issue across the world. Breast cancer is the most widelydiagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFSBOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

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Hatem A. Khater

Papers

A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Automatic detection of the pulmonary nodules from CT images

A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter

GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance USV navigation system based on adaptive DSFCF

A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection