Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : FFBPN

Detection of COVID-19 Based on Chest Medical Imaging and Artificial Intelligence Techniques

Nawres A. Alwash; Hussain Kareem

Engineering and Technology Journal, 2021, Volume 39, Issue 10, Pages 1588-1600
DOI: 10.30684/etj.v39i10.2200

The emergence of COVID-19 disease in the world has moved the wheel of scientific research in order to detect it in the best method, and the fastest of these methods is the use of Artificial Intelligence (AI) techniques to help medical professionals detect COVID-19. The proposed topic is aim to develop algorithm based on combination between imageprocessing techniques with artificial intelligence to diagnose COVID-19. The proposed algorithm consists of five stages to detect and classify COVID-19 from Computer Tomography (CT) images. These stages include; The first of these stages is to collect data from hospitals as real data and from Kagglewebsite for patients and healthy people, then the stage before removing the noise and converting it from RGB to grayscale, then we improve the image, segmentation and formalities, the other stage is a stage used to extract the important characteristics, and the last stage is the classification of images CT scan using Feed Forward Back Propagation Network (FFBPN) and Support Vector Machine (SVM )and compare the result between them and see if the person is infected or healthy. This study was implemented in MATLAB software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. As for the optimization technology, only the brightness of the images has been increased. At the stage of segmentation of the area of ​​lung injection using the area transplant method, the best results are detection of COVID-19 from other healthy tissues. The FFBPN gave the best results for detecting and classifying COVID-19 as well as determining whether a person has been infected or not. The results of the proposed methodology in accurate and rapid detection of COVID-19 in the lung. The contribution of this paper is to help medical staff detect COVID-19 without human intervention.

ECG Arrhythmias Classification by Combined Feature Extraction Method and Neural Network

Khalooq Y. Al Azzawi

Engineering and Technology Journal, 2014, Volume 32, Issue 3, Pages 586-596

Electrocardiogram (ECG) became one of the most crucial tool for heart status diagnosis. Generally, several arrhythmias may appear based on different heart rate or ECG signal morphology variation. In this paper, a novel combined feature extraction method to present ECG arrhythmias is proposed. The combination between Wavelet Packet Transform (WPT) entropies and Power Spectrum Density (PSD) is suggested. For classification, Feed Forward Backpropagation Neural Network (FFBPN) is utilized. The experimental results showed that the proposed method can be beneficial for ECG signal arrhythmias classification. MIT-BIH Arrhythmia Database was used for algorithm testing. The proposed method was compared with three state of art methods, where was of better performance reached about 80%. The proposed method as well as other methods was tested in noisy environment for comparison investigations. The suggested method is promising approach for arrhythmias classification. However, enormous testing data set might significantly improve the results.