Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

Left ventricular(LV)dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI.While these indices give information about the presence or not of LV alteration,they are not able to identify the location and the size of such alteration.The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction.The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function.Three parametric images approaches:covariance analysis,parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views.The results show that parametric images generated from the monogenic signal were superior in term of sensitivity(89.69%),specificity(86.51%)and accuracy(89.06%)to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction.Therefore,the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.

Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability

Breast cancer(BCa)and prostate cancer(PCa)are the two most common types of cancer.Various factors play a role in these cancers,and discovering the most important ones might help patients live longer,better lives.This study aims to determine the variables that most affect patient survivability,and how the use of different machine learning algorithms can assist in such predictions.The AURIA database was used,which contains electronic healthcare records(EHRs)of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland.In total,there were 178 features for BCa and 143 for PCa.Six feature selection algorithms were used to obtain the 21 most important variables for BCa,and 19 for PCa.These features were then used to predict patient survivability by employing nine different machine learning algorithms.Seventy-five percent of the dataset was used to train the models and 25%for testing.Cross-validation was carried out using the StratifiedKfold technique to test the effectiveness of the machine learning models.The support vector machine classifier yielded the best ROC with an area under the curve(AUC)=0.83,followed by the KNeighborsClassifier with AUC=0.82 for the BCa dataset.The two algorithms that yielded the best results for PCa are the random forest classifier and KNeighborsClassifier,both with AUC=0.82.This study shows that not all variables are decisive when predicting breast or prostate cancer patient survivability.By narrowing down the input variables,healthcare professionals were able to focus on the issues that most impact patients,and hence devise better,more individualized care plans.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide.This newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently available.Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus.Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup.Here,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation.First,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,respectively.Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused.Parallel architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was considered.The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale dataset.This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.