Heba Afify

Followers

Following

Public Views

Interests

Uploads

Papers by Heba Afify

Protein secondary structure prediction (PSSP) using different machine algorithms

Egyptian Journal of Medical Human Genetics

Background The computational biology approach has advanced exponentially in protein secondary str... more Background The computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics studies. In this paper, the support vector machine (SVM) model and decision tree are presented on the RS126 dataset to address the problem of PSSP. A decision tree is applied for the SVM outcome to obtain the relevant guidelines possible for PSSP. Furthermore, the number of produced rules was fairly small, and they show a greater degree of comprehensibility compared to other rules. Several of the proposed principles have compelling and relevant biological clarification. Results The results confirmed that the existence of a particular amino acid in a protein sequence increases the stability for the forecast of protein secondary structure. The suggested algorithm achieved 85% accuracy for the E|~E cla...

Download

3D reconstruction of structural magnetic resonance neuroimaging based on computer aided detection

International Journal of Bio-Inspired Computation

A biomechanical analysis of prosthesis disc in lumbar spinal segment using three-dimensional finite element modelling

International Journal of Biomedical Engineering and Technology

Comparative Biomechanical Analysis of Lumbar Disc Arthroplasty using Finite Element Modeling

2018 13th International Conference on Computer Engineering and Systems (ICCES)

Samir Y Marzouk Comparative Biomechanical Analysis of Lumbar Disc Arthroplasty using Finite Eleme... more Samir Y Marzouk Comparative Biomechanical Analysis of Lumbar Disc Arthroplasty using Finite Element Modeling Lumbar total disc replacement (LTDR) is a surgical procedure for the treatment of degenerative disc disease (DDD) and lumbar spinal distortion to preserve range of motion (ROM). The SB Charité™ is the first device for LTDR but it produces more complications and long-term issues, causing a shortage of SB Charité™ disc. Currently, the evolution of lumbar disc arthroplasty based on some criteria like prosthetics structure, biomechanical model, tissue engineering and biomaterials approach. The optimal biomechanical model is based on kinematic and kinetic parameters together to ensure a long-term implantation with a lower rate of damage from disc implant in the spinal column. The finite element method (FEE) of human lumbar spinal is advanced to support biomechanical modeling techniques which are related to biomaterials guideline for choosing better material for implantation. Therefore, this paper presented a 3D biomechanical FEM model of L1 to L3 lumbar spines by different types of discs and materials. We applied a compressive force, and compressive force plus extension moment to this model to calculate Tresca stress of annulus fibers, strain and von Mises stress on the vertebral endplate at each intervertebral level under COMSOL Multiphysics® software.

Download

Convolutional Neural Network with Batch Normalization for Classification of Endoscopic Gastrointestinal Diseases

Studies in Big Data, 2020

Classification of Human Sperm Head in Microscopic Images Using Twin Support Vector Machine and Neural Network

Advances in Intelligent Systems and Computing, 2020

This paper describes a computer-aided diagnosis (CAD) system for the classification of healthy/un... more This paper describes a computer-aided diagnosis (CAD) system for the classification of healthy/unhealthy human sperm heads based on texture analysis obtained from the microscopic images. This work is based on 60 human sperm microscopic images selected from the National Research Centre in Egypt. Texture analysis is done by using intensity histogram, Haralick’s Spatial Gray-Level Co-occurrence Matrix (SGLCM), statistical feature matrix (SFM), gray-level run length matrix (GLRLM), and invariant moment’s features. A total of forty-six texture features are computed for healthy as well as unhealthy region of interest (ROI). Since many numbers of the features may effect on accuracy of classification, optimal feature selection is done to eliminate the less informative and excessive features. The feature selection is adapted using twin support vector machine (TWSVM). The accuracy of these features in distinguishing healthy/unhealthy human sperm heads has been evaluated by back-propagation neural network (BPNN) and linear support vector machine (SVM) classifiers. From the results analysis, it was found that ANN classifier gave an overall classification accuracy of 100% with 100% sensitivity. The results show that it is feasible to identify human sperm heads based on texture features extracted from microscopic images. This CAD system is shown to be useful for increased accuracy and increased speed for the classification of healthy/unhealthy sperm heads for improving the fertilization process.

A 3D image segmentation for lung cancer using V.Net architecture based deep convolutional networks

Journal of Medical Engineering & Technology, 2021

Abstract Lung segmentation of chest CT scan is utilised to identify lung cancer and this step is ... more Abstract Lung segmentation of chest CT scan is utilised to identify lung cancer and this step is also critical in other diagnostic pathways. Therefore, powerful algorithms to accomplish this accurate segmentation task are highly needed in the medical imaging domain, where the tumours are required to be segmented with the lung parenchyma. Also, the lung parenchyma needs to be detached from the tumour regions that are often confused with the lung tissue. Recently, lung semantic segmentation is more suitable to allocate each pixel in the image to a predefined class based on fully convolutional networks (FCNs). In this paper, CT cancer scans from the Task06_Lung database were applied to FCN that was inspired by V.Net architecture for efficiently selecting a region of interest (ROI) using the 3D segmentation. This lung database is segregated into 64 training images and 32 testing images. The proposed system is generalised by three steps including data preprocessing, data augmentation and neural network based on the V-Net model. Then, it was evaluated by dice score coefficient (DSC) to calculate the ratio of the segmented image and the ground truth image. This proposed system outperformed other previous schemes for 3D lung segmentation with an average DCS of 80% for ROI and 98% for surrounding lung tissues. Moreover, this system demonstrated that 3D views of lung tumours in CT images precisely carried tumour estimation and robust lung segmentation.

A Modified Inception-v4 for Imbalanced Skin Cancer Classification Dataset

2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019

Deep learning architectures, especially deep convolutional neural networks (CNN) achieve high acc... more Deep learning architectures, especially deep convolutional neural networks (CNN) achieve high accuracy on object classification and localization tasks. Achieving such high accuracy requires powerful devices. In this paper, rather than an ensemble of multiple complex models, a single Inception-v4 model is adapted to classify extracted from the HAM10000 dataset. The proposed model is enhanced by employing feature reuse using long residual connection in which the features extracted from earlier layers are concatenated with the high-level layers to increase the model classification performance. The dataset used in this study is imbalanced; therefore, a data sampling approach is used to mitigate the data imbalance effect. The proposed architecture achieves an accuracy of 94.7% using the provided test set at the official benchmark for the International Skin Imaging Collaboration (ISIC) 2018.

A 3-D biomechanical model for enhancing the performance of artificial hand

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

The hand is a complex organ that is precious to humans. It is hard to replace any living organ su... more The hand is a complex organ that is precious to humans. It is hard to replace any living organ such as a hand with a fully adequate substitute. However, during the last decades, many technologies of artificial hand have been enhanced and better models have been introduced. Many factors largely depend on the modeling of artificial hand such as person's occupation (e.g. athletes, engineers), types of motion (linear, angular), the strength of muscles, weight of loading on arms, stability of moving and materials of the prosthesis. The reasons for limb loss are diversified from injury, amputation to disability, thus treatment of the hand is powered by assessment of the condition enabling this different clientele, environmental factors, anatomical structure, and lifestyle. This paper presents solid models of artificial hand that made fully out of four different metals including aluminum, copper, steel, and titanium. The simulation outcome helps to decide whether materials to choose. This research aims to design a three-dimensional (3-D) model by COMSOL Multiphysics 5.2 software which is able to clarify weaknesses and strengths of the artificial hand. The artificial hand is analyzed to study the strain, stress, and forces on joints for two cases, including free artificial hand under effect of the gravity and artificial hand gripping a ball. Results confirmed that the titanium is the best material for implementation of this model. This model will affect the manipulation of robotic design.

Applied Biomechanics and Bioinformatics for Analysis of Limb Lengthening Research

Biomechanics demonstrated by the variety of topics associated with physical therapy such as exerc... more Biomechanics demonstrated by the variety of topics associated with physical therapy such as exercise, sports, neuromuscular control, energetics, and locomotor apparatus. Biomechanical treatment related to physical and mechanical components of the patient. Physical therapists are educated in the bio aspect of treatment, whereas prosthetists/orthotists are educated in the mechanical aspect. Physical therapy need to become more familiar with mechanical treatment and learn how to integrate this into their physical treatment program. Orthotics must become more familiar with the importance of physical treatment and the internal corrective forces necessary for efficient ambulation. In order to develop biomechanical treatment, an understanding of genetic factors that based on responsible genes for bone formation, is needed. Limb lengthening is used increasingly for children who have unequal lengths. It is critical for maintenance of joint motion that can be difficult to achieve. The Nationa...

Biomechanical analysis of electric powered wheelchair by reverse engineering modelling technology

Biomedical Research-tokyo, 2017

Wheelchair technology is one of the most significant technologies in biomechanics sector. The dem... more Wheelchair technology is one of the most significant technologies in biomechanics sector. The demand of Electric Powered Wheelchairs (EPWs) has been increasing due to improving the lifestyles of patients suffering from one or more disabilities, including motor control problems, spinal injuries, arthritis, balance disorders and amputation under the term of rehabilitation technologies. Hence, the healthcare research has made several plans for improving control of EPW to provide well-being, and safety by minimal cost. On the other hand, Reserve Engineering (RE) is a powerful technique for creating a new model to uncover the features of commercial products and enhance them. As such, we applied RE to develop a functional model through teardowns that designed by SolidWorks 3D Computer Aided Design (CAD) software to provide the full specifications of EPW. Moreover, we implemented an Arduino microcontroller to change the EPW directionality by using a joystick. This model evaluates the flexi...

Download

Abstract-methods of Classification of Cardiac Arrhythmias Have Decisive Influence on Performance of All Electrocardiography (ecg) Signal Processing Systems. This Work Presents a New Framework for Nonlinear Pattern Recognition /classification of Ecg Based on the N-dimensional Moment Invariants Record

approach of applying the theory of moment invariants to analyze ECG used to improve the accuracy ... more

Download

Automatic Scoring and Grading of COVID-19 Lung Infection Approach

Although the successful detection of COVID-19 from lung computed tomography (CT) image mainly dep... more Although the successful detection of COVID-19 from lung computed tomography (CT) image mainly depends on radiologist’s experience, specialists occasionally disagree with their judgments The performance of COVID-19 detection models needs to be improved According to COVID-19 symptoms and human immune approach response, there are four types of its contagion such as asymptomatic, mild, severe, and recovered In this chapter, an automatic scoring of COVID-19 lung infection grading approach is presented The proposed approach is based on a combination of image segmentation techniques and the Particle Swarm Optimization (PSO) algorithm to access accurate evaluation for infection rate Fuzzy c-means, K-means and thresholding-based segmentation algorithms are used for isolating the chest lung from the CT images Then, PSO is used with the three segmentation algorithms for clustering the region of interest (ROI) that consists of COVID-19 infected regions in lung CT Then, scoring the infection rat...

A Comparative Study of Protein Sequences Classification-Based Machine Learning Methods for COVID-19 Virus against HIV-1

Applied Artificial Intelligence, 2021

ABSTRACT The effective spread of COVID-19 cases in several countries produces more protein sequen... more ABSTRACT The effective spread of COVID-19 cases in several countries produces more protein sequences that are released in genomic public sources. It provides some awareness and indications for virus classification of COVID-19 and HIV-1 that are essential for drug discovery of COVID-19. This paper reveals the importance of machine learning algorithms to handle the recognition of two different viruses. Therefore, 18,476 protein sequences for both COVID-19 and HIV-1 and 9238 for each virus are applied to the proposed model based on feature extraction, data labeling, and six classifiers. Amino acid classification according to their dipoles and volumes is employed as a feature extraction tool based on the creation of eight features from twenty amino acids by using the conjoint triad (CT) method. The data labeling is employed as a coding tool by binary numbers refereeing zero for COVID-19 and one for HIV-1. The random forest (RF) model achieved the highest classification accuracy of 99.89% for eight features and 97.80% for two features. The experimental results significantly confirmed that eight features required more computational time than two features, but the accuracy rate was nearly similar in the two cases. This classification strategy of COVID-19 and HIV-1 will prompt the prediction of protein sequences of the new virus.

Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model

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

The performance recognition of bacteria cell images is an effective survey for treatment of vario... more The performance recognition of bacteria cell images is an effective survey for treatment of various diseases caused by the bacteria. Many algorithms for bacteria classification are designed for the needs of analysis of large-scale microscopic image bacteria. However, the biologist interpretation is suffered from insufficient information and thus may lead to limited accuracy in the bacteria classification process. To handle this drawback, machine learning tools, and image analysis approaches tackled identification of different bacteria species for improving the clinical microbiology investigation. In the proposed study, 200 bacterial images for ten different bacteria species with 20 images for each specie are extracted from DIBaS (Digital Images of Bacteria Species dataset). This proposed framework is divided into image preprocessing phase which obtained by histogram equalization, feature extraction by Bag-of-words model and classification phase by Support Vector Machine (SVM). The main objective is to enhance the bacterial images and find the image feature descriptors from the enhanced images which allowing to classify the bacterial images. The experimental results provided an average accuracy of 97% with classifier speed for automated detection and classification of bacterial images which would greatly reduce the disease outbreaks in future researches.

An improved framework for polyp image segmentation based on SegNet architecture

International Journal of Imaging Systems and Technology, 2021

A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The det... more A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir‐SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polyp image. This paper has demonstrated state‐of‐the‐art performance on both VGG‐16, and VGG‐19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG‐19 model has outperformed the VGG‐16 model via all evaluation parameters except sensitivity for the polyp segmentation on the Kvasir‐SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time.

Artificial Intelligent System for Skin Diseases Classification

Biomedical Engineering: Applications, Basis and Communications, 2020

In this paper, an artificial intelligent technique is proposed for skin disease detection and cla... more In this paper, an artificial intelligent technique is proposed for skin disease detection and classification. The suggested method comprises four stages, including segmentation, extraction of textural features, and classification. The stretch-based enhanced algorithm has been adapted for image enhancement. Then the method of an active contour is used for segmentation to determine the skin lesion in tissue. Textural features are obtained from the segmented skin lesion. As several numbers of the features can affect the classification precision, ideal feature selection is made to exclude features that are less informative and unnecessary. The feature selection is adjusted with a regularized random forest. Finally, the classification algorithms by support vector machine and a back-propagation neural network (BPNN) are implemented. The dataset consists of 400 dermoscopic images in total divided into 200 benign and 200 malignant skin diseases extracted from the dermoscopic images PH2 data...

An Automated CAD System of CT Chest Images for COVID-19 Based on Genetic Algorithm and K-Nearest Neighbor Classifier

Ingénierie des systèmes d information, 2020

The detection of COVID-19 from computed tomography (CT) scans suffered from inaccuracies due to i... more The detection of COVID-19 from computed tomography (CT) scans suffered from inaccuracies due to its difficulty in data acquisition and radiologist errors. Therefore, a fully automated computer-aided detection (CAD) system is proposed to detect coronavirus versus non-coronavirus images. In this paper, a total of 200 images for coronavirus and non-coronavirus are employed based on 90% for training images and 10% testing images. The proposed system comprised five stages for organizing the virus prevalence. In the first stage, the images are preprocessed by thresholding-based lung segmentation. Afterward, the feature extraction technique was performed on segmented images, while the genetic algorithm performed on sixty-four extracted features to adopt the superior features. In the final stage, the K-nearest neighbor (KNN) and decision tree are applied for COVID-19 classification. The findings of this paper confirmed that the KNN classifier with K=3 is accomplished for COVID-19 detection ...

Download

Multi- Images Recognition of Breast Cancer Histopathological via Probabilistic Neural Network Approach

Journal of System and Management Sciences, 2020

This paper suggests an approach for automated diagnosis of different types of breast carcinoma hi... more This paper suggests an approach for automated diagnosis of different types of breast carcinoma histopathological images via machine learning algorithms. The approach starts by extracting some features then use the nondominated genetic algorithm II (NSGA-II) algorithm and Ant Colony Optimization (ACO) to select the best features. Additionally, two classifiers including probabilistic neural network (PNN) and multi-class support vector machine (MSVM) is proposed to determine the type of breast cancer. These histopathological images database consider as a multi-classification task that used 848 BreakHis images with 400× magnification factors. The data divided into 160 samples for the testing phase and 688 samples for the training phase. The experiment results evaluated by four parameters including accuracy, sensitivity, specificity, and precision. For the NSGA-II algorithm, these outcomes refer to the maximum accuracy obtained through the combined features set with the accuracy being 100% by the training dataset and 82.5% by the testing dataset for the PNN classifier.

Download

Computer-Aided Diagnosis of Speech Disorder Signal in Parkinsonâ€™s Disease

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2016

Computer-aided diagnosis (CAD) can be used as a decision support system by physicians in the diag... more Computer-aided diagnosis (CAD) can be used as a decision support system by physicians in the diagnosis and treatmentof disordered speech especially those who specialize in neurophysiology diseases. Parkinson's disease (PD) is aprogressive disorder of the nervous system that affects movement. It develops gradually, sometimes starting with a barelynoticeable tremor in speech. It has been found that 80% of persons with PD reported speech and voice disorders.Parkinson's disease symptoms worsen as the condition progresses over time. Therefore, Speech may become soft orslurred and these deficits in speech intelligibility impact on health status and quality of life. Different researchers arecurrently working in the analysis of speech signal of people with PD, including the study of different dimensions in speechsuch as phonation, articulation, prosody, and intelligibility. Here, we present the characteristics and features of normalspeech and speech disorders in people with PD and t...

Download

Heba Afify

Uploads

Papers by Heba Afify

Log In