I received B.Sc. and M.Sc. degrees in Computer Science from University of Baghdad, IRAQ. I worked in Department of Computer Science/ College of Science as teaching assistance during 1991-2000 and as a lecturer through the period of 2004-2008. I received my PhD in Intelligent Computing from Universiti Putra Malaysia (UPM) in Feb. 2014. My research in the field of computational methods in computer vision and medical image analysis, especially on Technical Application of Echocardiography images.
EDUCATION
PhD in Intelligent Computing , University Putra Malaysia -2014
M.Sc in Computer Science , University of Baghdad, Iraq- 2004
B.Sc in Computer Science , , University of Baghdad, Iraq- 1987
RESEARCH INTERESTS
Intelligent Computing, Computer Vision, Biomedical engineering, Medical Image analysis and processing, Software engineering
FUTURE PROJECTS
Deep Learning
Applications Invited
9
Scopus Publications
Scopus Publications
Face-based Gender Classification Using Deep Learning Model Buraq Abed Ruda Hassan, Faten Abed Ali Dawood Journal of Engineering Iraq, 2024 Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer learning, a powerful deep learning technique that can be effectively employed to Face gender classification using the Alex-Net architecture. The performance evaluation of the proposed gender classification model encompassed three datasets: the LFW dataset, which contained 1,200 facial images. The Faces94 dataset contained 400 facial images, and the family dataset had 400. The Transfer Learning with the Alex-Net model achieved an accuracy of 98.77% on the LFW dataset. Furthermore, the model attained an accuracy rate of 100% on both the Faces94 and family datasets. Thus, the proposed system emphasizes the significance of employing pre-processing techniques and transfer learning with the Alex-Net model. These methods contribute to more accurate results in gender classification. Where, the results achieved by applying image contrast enhancement techniques, such as HE and CLAHE, were compared. CLAHE achieved the best facial classification accuracy compared to HE.
Diagnosis and classification of type ii diabetes based on multilayer neural network Ekhlas S. Nasser, Faten Abd Ali Dawood Iraqi Journal of Science, 2021 Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments.
 The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, of which 240 are diabetic and 42 are non-diabetic patients. The model consists of three main phases. In the first phase, two steps are applied as a pre-processing for the dataset, which include statistical analysis and missing values handling. In the second phase, feature extraction is used for diabetes Type II using three main features, reflecting measurements of three blood parameters (C. peptide, fasting Blood Sugar, and Haemoglobin A1C). Finally, classification and performance evaluation are implemented using Feed Forward Neural Network algorithm. The experimental results of the performance of the proposed model showed 98.6% accuracy for diabetes classification.
Automatic pectoral muscles detection and removal in mammogram images Sarah Siham Fadhil, Faten Abed Ali Dawood Iraqi Journal of Science, 2021 
 
 
 
 
 
 The main aim of the Computer-Aided Detection/Diagnosis system is to assist the radiologists in examining the digital mammograms. Digital mammogram is the most popular screening technique for early detection of breast cancer. One of the problems in breast mammogram analysis is the presence of pectoral muscles region with high intensity in the upper right or left side of most Media-Lateral Oblique views of mammogram images. Therefore, it is important to remove this pectoral muscle from the image for accurate diagnosis results. The proposed method consists of three main steps. In the first step, noise is reduced using Median filtering. In the second step, artifacts removal and breast region extraction are performed using Otsu method. Finally, the pectoral muscle is extracted and removed using the proposed Split Orientation Local Thresholding (SOLTH) algorithm. For this study, a total of 110 mammogram images from the Mini-Mias database (MIAS) were used to evaluate the proposed method’s performance. The experimental results of automatic pectoral muscle detection and removal were observed by radiologist and showed 90.9% accuracy of acceptable results.
 
 
 
 
 
Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis Ammar I. Shihab, Faten A. Dawood, Ali H. Kashmar Advances in Bioinformatics, 2020 Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.
A hybrid method for endocardial contour extraction of right ventricle in 4-slices from 3D echocardiography dataset Faten A. Dawood, Rahmita W. Rahmat, Suhaini B. Kadiman, Lili N. Abdullah, Mohd D. Zamrin Advances in Bioinformatics, 2014 This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.
Border detection of ventricle wall motion in echocardiographic images: A survey Rahmita W. Rahmat, Faten A. Dawood, Suhaini B. Kadiman, Lili N. Abdullah, Mohd D. Zamrin Proceedings 2012 International Conference on Advanced Computer Science Applications and Technologies Acsat 2012, 2012 Echocardiography imaging is one of the most widely used diagnostic tests for cardiovascular diseases which allow direct visualization of cardiac structure and ventricles wall motion. It can provide useful information, including the size and shape of the heart. An accurate method for border detection of ventricle wall motion is still important clinical diagnosis tool. Therefore, most of common clinical parameters measurement has become a difficult challenge for many interested researchers especially in the field of Computer Aided Diagnostic (CAD). This paper reviews a number of investigative methods for border detection focusing on segmentation techniques developed in Two-dimensional echocardiographic images.
Automatic boundary detection of wall motion in two-dimensional echocardiography images Dawood Journal of Computer Science, 2011 Problem statement: Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is “important clinical diagnosis parameter for many cardiovascular diseases”. A challenge most researchers faced is how to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the pre-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain features of interest (i.e., myocardial tissue). In the second stage, we applied the segmentation process using thresholding technique by considering a mean value of pixels intensity as a threshold value to distinct image features (e.g., Background and object). After thresholding implementation, the two most common mathematical morphology operators ‘erosion’ and ‘dilation’ are applied to improve the efficiency of the wall boundary detection process. Finally, Robert’s operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of the proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance accuracy percentages are 50% more acceptable and 42% better than QLab system results.
