A novel facial emotion recognition scheme based on graph mining

COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). COVID-19 is a very widespread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients. Computed Tomography (CT) plays an important role in the diagnosis of COVID-19. As chest radiography can give an indicator of coronavirus. Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article. It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia. The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence. Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting. After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result. Support vector machine ensemble models are then built based on the extracted feature vectors. Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers. Experimental findings show an accuracy of 98.04 %. It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors.

Section: Experimental Results and Analysismentioning

confidence: 99%

Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence

Mohammed¹,

Alkinani²,

Hassan³

2020

“…On the other hand; different experiments are conducted to study the effect of changing the value of Sc, R, and parameters on the final accuracy of the proposed system. The classification accuracy can be computed as follows [18]:…”

Section: Resultsmentioning

confidence: 99%

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Ali¹,

Mohammed²

2021

Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.

“…On the other hand; experiments are performed to study the effect of changing the value of Bs parameter on the final accuracy of the proposed SER system. The measure that is used in system evaluation is the classification accuracy which can calculate as follows [16,17]:…”

Section: Resultsmentioning

confidence: 99%

Speech Emotion Recognition Using MELBP Variants of Spectrogram Image

Mohammed¹,

Alia²

2020

Speech emotion recognition finds many applications in the daily life like conversational agents, human robot interaction, call centres etc. However; the task of emotion recognition from speech signal is not trivial due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the signal in an accurate manner. Image processing techniques are exploited in this paper to solve speech emotion recognition problem. After converting the signal into 2D spectrogram image representation, four forms of Extended Local Binary Pattern (ELBP) are generated to serve as a source for feature extraction stage. The histograms of multiple blocks from ELBP variants are computed and fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed using Surrey AudioVisual Expressed Emotion (SAVEE) database and the achieved results showed that when using combined vectors of MELBP, the system gives the best accuracy which is 72.14%. The achieved result outperforms state-of-the-art results on the same database.