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The COVID-19 pandemic has severely impacted global healthcare and financial systems, Highlighting the need for an automatic computer-aided diagnosis system using image recognition for chest X-rays(CXR). This study aims to classify COVID-19, normal, and pneumonia patients from CXR images via a modified ResNet-50 pre-trained CNN model. Our experiments are based on Dataset-1, which contains CXR images of COVID-19 and normal, while Dataset-2 also includes pneumonia. Dataset-1 and Dataset-2 were collected from King Abdul-Aziz Medical City in National Guard, Jeddah, Saudi Arabia. Moreover, a sample from the Kaggle repository was added to Dataset-1 and 2 to make two more Datasets. Our results for diagnosis of raspatory diseases have shown reliability and high accuracy of 95.28% (97.66% Sensitivity and 93.12% Specificity), which will be beneficial in aiding physicians and healthcare centres in the global fight against harmful spreading viruses through employing AI and ML techniques in X-ray medical diagnosis. [ABSTRACT FROM AUTHOR]

A hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson's Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension. [ABSTRACT FROM AUTHOR]

An automated system for early diagnosis of type 2-diabetes mellitus is proposed in this paper, by using the Extreme Learning Machine neural network for classification and the evolutionary genetic algorithms for feature extraction, to be employed on a real data set from Saudi Arabian patients. The dimension of the feature space is reduced by the genetic algorithms and only the effective features are selected. The data is then fed to an Extreme Learning Machine neural network for classification. Diabetes is a major health problem in both industrial and developing countries, and when it appears in pregnancies it may cause many complications, hence its early diagnosis is beneficial for both mother and fetus. Our hybrid algorithm, the GA-ELM algorithm, has produced an optimized diagnosis of type 2-diabetes patients and classified the data set with an accuracy of 97.5% and with six effective features, out of the original eight features given in the dataset. Moreover, comparisons of the GA-ELM method with other available methods were conducted and the results are promising. [ABSTRACT FROM AUTHOR]