Manal Abdel Wahed | Cairo University (original) (raw)

Papers by Manal Abdel Wahed

Algorithms for intelligent systems, 2022

Journal of Engineering and Applied Science, Dec 1, 2022

Microarray technology is an effective tool for advanced biomedical studies. It can be applied to ... more Microarray technology is an effective tool for advanced biomedical studies. It can be applied to quality expression (GE) profiling, which is used to measure the expression levels of thousands of qualities on a single chip in a trial. However, missing values (MVs) may be encountered during processing because of environmental, specialized, and natural reasons, such as spotting issues, foundation commotion, counting errors, inadequate determination, picture debasement, clean or scratches on a slide, and methodical causes; thus, mechanical strategies should be developed, but applying any feature selection technique on incomplete microarray data poses a problem because most techniques fail. Many studies have shown that microarray data sets can contain up to 10% of missing data and up to 90% of genes with one or more missing data in some cases [1, 2]. Handling missing data is a challenge for researchers classifying cancers because these data should be imputed for information consideration. They are also used to understand the overall data and perform complicated tasks, such as predictive analysis and data protection against distortion.

Journal of Advanced Engineering Trends

Lung cancer has a high incidence rate and is considered highly fatal because of its low survival ... more Lung cancer has a high incidence rate and is considered highly fatal because of its low survival rate at early stages compared to other cancers. Computed tomography (CT) scans can reveal pulmonary nodules of different shapes and volumes in two dimensional (2D) slices. Three-dimensional (3D) reconstruction of pulmonary nodules can assist the radiologist in early treatment appropriate for the 3D nodule volume screened. In this research, we present a 3D reconstruction algorithm that uses 2D CT slices to reconstruct a 3D lung nodule. The equivalent diameters of small nodules ranged from 3 to 30 mm. A segmentation approach (based on bounding boxes and maximum intensity projection) was applied. Extracting the lung nodules from the 2D candidate masses was performed via a rule-based classifier. Surface rendering was used to reconstruct 3D pulmonary nodules which were visualized on the 3D Slicer software. The 3D nodule volume, as well as the accuracy rate and error of volume estimation were calculated. The proposed methodology was validated against the actual volumes of 14 3D nodules from the Lung Image Database Consortium (LIDC) database. The proposed algorithm achieved a maximum accuracy of 99.6627 % for lung nodule volume estimation. The corresponding average accuracy rate and average percentage error were 97.34 % and 2.66 %, respectively. The screening of 3D lung nodules can support surgery planning via nodule volume estimation. The average accuracy and error rates of the 3D reconstruction algorithm showed promising results in comparison with other published studies.

IEEE Access

Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal... more Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal with the difficulties associated with outlier detection across various domains and data characteristics, ensembles of outlier detectors have recently been employed to improve the performance of individual outlier detectors. In this paper, we follow an ensemble outlier detection approach in which good outlier detectors are selected through an enhanced clustering-based dynamic selection (CBDS) method. In this method, a bisecting K-means clustering algorithm is employed to partition the input data into clusters where every cluster defines a local region of competence. Among the initial pool of detectors, the outputs of the detectors with the most competent local performance were combined through four possible schemes to produce the final OD results. Experimental evaluation and comparison of our method were carried out against four variants of locally selective combination in parallel (LSCP) outlier ensembles. The CBDS-based schemes compare well with the LSCP-based ones on 16 public benchmark datasets and incur considerably lower computational costs. The CBDS method consistently achieved superior average scores of the area under the curve (AUC) of the receiver operating characteristic (ROC), and particularly outperformed the LSCP method on nine of the 16 datasets in terms of the AUC score. In addition, while the CBDS and LSCP methods have similar computational costs on small datasets, the CBDS method achieves significant time savings compared with the LSCP method on large datasets. INDEX TERMS Bisecting K-means, dynamic detector selection, outlier detection, outlier ensemble.

PLOS ONE

Quantitative grading and classification of the severity of facial paralysis (FP) are important fo... more Quantitative grading and classification of the severity of facial paralysis (FP) are important for selecting the treatment plan and detecting subtle improvement that cannot be detected clinically. To date, none of the available FP grading systems have gained widespread clinical acceptance. The work presented here describes the development and testing of a system for FP grading and assessment which is part of a comprehensive evaluation system for FP. The system is based on the Kinect v2 hardware and the accompanying software SDK 2.0 in extracting the real time facial landmarks and facial animation units (FAUs). The aim of this paper is to describe the development and testing of the FP assessment phase (first phase) of a larger comprehensive evaluation system of FP. The system includes two phases; FP assessment and FP classification. A dataset of 375 records from 13 unilateral FP patients was compiled for this study. The FP assessment includes three separate modules. One module is the...

2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES)

Neural Computing and Applications

Lung cancer is one of the most serious cancers in the world with the minimum survival rate after ... more Lung cancer is one of the most serious cancers in the world with the minimum survival rate after the diagnosis as it appears in Computed Tomography scans. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other structures such as blood vessels or the pleura. Diagnosis of lung nodules according to their location increases the survival rate as it achieves diagnostic and therapeutic quality assurance. In this paper, a Computer Aided Diagnosis (CADx) system is proposed to classify solitary nodules and juxtapleural nodules inside the lungs. Two main auto-diagnostic schemes of supervised learning for lung nodules classification are achieved. In the first scheme, (bounding box + Maximum intensity projection) and (Thresholding + K-means clustering) segmentation approaches are proposed then first- and second-order features are extracted. Fisher score ranking is also used in the first scheme as a feature selection method. The higher five, ten, and fifteen ranks of the...

BioMedical Engineering OnLine

Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activit... more Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was develop...

Biomedical Engineering / Biomedizinische Technik

The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnor... more The incidence of vision impairment is rapidly increasing. Diagnosis and classifying retinal abnormalities in ophthalmological applications is a significant challenge. Using Optical Coherence Tomography (OCT), the study aims to develop a computer aided diagnosis system for detecting and classifying retinal disorders. Choroidal neovascularization, diabetic macular edema, drusen, and normal cases are the investigated groups. Both deep learning and machine learning are combined to build the system. The SqueezeNet neural network was modified to extract features. The Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) algorithms were used for disorder classification. The Bayesian optimization technique was also used to determine the best hyperparameters for each model. The model’ performance was evaluated through nine criteria using 12,000 OCT images. The results have demonstrated accuracies of 97.39, 97.47, 96.98, and 95.25% for the SVM, K...

Alzheimer's disease (AD) is the most common form of dementia with strong genetic factors in w... more Alzheimer's disease (AD) is the most common form of dementia with strong genetic factors in which a combination of genetic variants contributes to AD risk. Discovering epistasis interactions among genetic variants is key to identifying valuable AD predictive models that allow earlier diagnosis and better prognosis for patient. Presently, AD predictive models are derived using either statistical or biological feature selection methods. Unfortunately, both approaches suffer from inherent limitations in their generalization and prediction power. This study presents a new hybrid method between these two approaches based on integrated higher-order evidence-based (IHOEB) framework. This method combines statistical and biological feature selection methods and allow computationally-efficient detection of up to 4-way epistasis models associated with AD. The new processing framework was applied to data obtained from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). The clas...

k-space.org, 2006

... ECG CLASSIFICATION USING AFFINE INVARIANT CHARACTERIZATION OF PHASE SPACE Heba Afify, Manal A... more ... ECG CLASSIFICATION USING AFFINE INVARIANT CHARACTERIZATION OF PHASE SPACE Heba Afify, Manal A. Wahed, Yasser M. Kadah Biomedical Engineering Department, Faculty of Engineering, Cairo ... [8] SO Belkasim, M. Shridhar, and M. Ahmadi, Pattern recognition ...

2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), 2018

Journal of Engineering Research, 2021

Emergency department is the first admission point for urgent patients. It is characterized by the... more Emergency department is the first admission point for urgent patients. It is characterized by the overcrowding due to the functional operations of this department. Indeed, sometimes one minute for an emergency department patient means life sustainability. One form to cope with the overcrowding problem is to reduce patient waiting time. Therefore, the study aims to reduce the average waiting time for all types of emergency department patients by using quality function deployment. It starts with involvement of all stakeholders within the department. The stakeholders include patients, nurses, doctors, and clinical engineers. The framework was designed through six steps, commencing with customer (patients) requirements identification, and concluding with the weights of technical requirements. The model has been applied on 2 hospitals. Paired samples t-test results reveal a significant reduction in the average waiting time, increasing the served patients and improving the quality of emer...

Biomedical Physics & Engineering Express, 2021

Quantitative assessment and classification of facial paralysis (FP) are essential for treatment s... more Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial functions: smiling, eye closure, raising the eyebrows, blowing cheeks, and whistling as well as the rest state. 3D facial landmarks and facial animation units (FAUs) were obtained using the Kinect V2, a fast and cost-effective depth camera. These were used to compute the features used in a Support Vector Machine (SVM) classifier. A dataset of 1650 records from 50 normal subjects was compiled for this study. The performances of different SVM kernel models were tested with different feature groups. The best performance (Accuracy = 96.7%, Sensitivity = 90.2%, and Specificity = 98%) was found when using the RBF kernel model applied on just nine differences in FAUs. This research will be developed and extended to include FP classification.

2018 9th Cairo International Biomedical Engineering Conference (CIBEC), 2018

Emergency department (ED) is one of the most significant departments in healthcare facilities. Or... more Emergency department (ED) is one of the most significant departments in healthcare facilities. Organizing patient flow in this department is a critical issue. However, large number of patients suffering from overcrowding as a result of a series of causes such as long waiting time, overburdened working staff, and delayed treatment. The purpose of this study is to develop a novel approach that improves patient flow within ED. Quality function deployment (QFD) has been employed by a set of factors to resolve the overcrowding problem in ED. The improvement of patient flow can be indicated by patient’s waiting time reduction. The results demonstrate the consistency of the proposed framework. The waiting time of different patients’ categories is adequately reduced which reflects the improvement of patient flow and consequently quality of the service in ED.

Biomedical Engineering: Applications, Basis and Communications, 2020

Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the l... more Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78[Formula: see text]mm to 22.48[Formula: see text]mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: [Formula: see ...

Journal of healthcare engineering, 2018

Medical imaging equipment (MIE) is the baseline of providing patient diagnosis in healthcare faci... more Medical imaging equipment (MIE) is the baseline of providing patient diagnosis in healthcare facilities. However, that type of equipment poses high risk for patients, operators, and environment in terms of technology and application. Considering risk management in MIE management is rarely covered in literature. The study proposes a methodology that controls risks associated with MIE management. The methodology is based on proposing a set of key performance indicators (KPIs) that lead to identify a set of undesired events (UDEs), and through a risk matrix, a risk level is evaluated. By using cloud computing software, risks could be controlled to be manageable. The methodology was verified by using a data set of 204 pieces of MIE along 104 hospitals, which belong to Egyptian Ministry of Health. Results point to appropriateness of proposed KPIs and UDEs in risk evaluation and control. Thus, the study reveals that optimizing risks taking into account the costs has an impact on risk cont...

Current Medical Imaging Reviews, 2018

Journal of Medical Imaging and Health Informatics, Sep 1, 2011

Journal of Medical Imaging and Health Informatics, 2014