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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Qanita Bani Baker
Jordan University of Science and Technology
Computer Science & IT Electrical & Electronics Engineering

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Lumbar disk 3D modeling from limited number of MRI axial slices Asma’a Al-Mnayyis; Sanaa Abu Alasal; Mohammad Alsmirat; Qanita Bani Baker; Shadi AlZu’bi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.954 KB) | DOI: 10.11591/ijece.v10i4.pp4101-4108

Abstract

This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patients MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the the region of interest. The validation of our 3D models is based on a radiologist’s analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to more accurate and easy diagnosis process.
A transfer learning with deep neural network approach for diabetic retinopathy classification Mohammed Al-Smadi; Mahmoud Hammad; Qanita Bani Baker; Sa’ad A. Al-Zboon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3492-3501

Abstract

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
Transfer deep learning approach for detecting coronavirus disease in X-ray images Mohammed Al-Smadi; Mahmoud Hammad; Qanita Bani Baker; Saja Khaled Tawalbeh; Sa’ad A. Al-Zboon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4999-5008

Abstract

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.
Using deep learning models for learning semantic text similarity of Arabic questions Mahmoud Hammad; Mohammed Al-Smadi; Qanita Bani Baker; Sa’ad A. Al-Zboon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3519-3528

Abstract

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herniation using 3D modeling Sanaa Abu Alasal; Mohammad Alsmirat; Asma’a Al-Mnayyis; Qanita Bani baker; Mahmoud Al-Ayyoub
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4336-4344

Abstract

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy.
Forecasting epidemic diseases with Arabic Twitter data and WHO reports using machine learning techniques Qanita Bani Baker; Farah Shatnawi; Saif Rawashdeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3447

Abstract

Twitter is one of the essential social media tools used by many people because they express their views, daily problems, and what they suffer from the health aspects. On Twitter, we can detect and track the spread of the most serious diseases like flu; by analyzing people's tweets and collecting reports from health organizations. In this paper, the data from Twitter was collected in the Arabic language related to the spread of influenza using many Arabic keywords. Then, we applied several machine learning algorithms, which are random forest, multinomial naïve bayes, decision tree, and voting classifier. We also found the correlation between the collected tweets and the reports collected from the World Health Organization (WHO) website according to three experiments. These experiments are: i) between the tweets and reports based on the 13 countries regardless of the time, ii) between the tweets and reports based on the Arab regions that depend on these countries' dialects irrespective of the time, iii) between all tweets and all reports based on the week number. The results from these experiments show that there is a strong correlation between the tweets and the reports, which means that the tweets and the WHO reports can together detect the flu outbreaks in the Arab world.
Towards enhancing the user experience of ChIP-Seq data analysis web tools Mahmoud Hammad; Qanita Bani Baker; Mohammed Al-Smadi; Wesam Alrashdan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5236-5247

Abstract

Deoxyribonucleic acid (DNA) sequencing is the process of locating the sequence of the main chemical bases in the DNA. Next-generation sequencing (NGS) is the state-of-the-art DNA sequencing technique. The NGS technique advanced the biological science in analyzing human DNA due to its scalability, high throughput, and speed. Analyzing human DNA is crucial to determine the ability of a person to develop certain diseases and his ability to respond to certain medications. ChIP-sequencing is a method that combines chromatin immunoprecipitation (ChIP) with NGS sequencing to analyze protein interactions with DNA to identify binding sites. Many online web tools have been developed to conduct ChIP-Seq data analysis to either discover or find motifs, i.e., patterns of binding sites. Since these ChIP-Seq web tools need to be used by clinical practitioners, they must comply to the web-related usability tasks including effectiveness, efficiency and satisfaction to enhance the user experience (UX). To that end, we have conducted an empirical study to understand their UX design. Specifically, we have evaluated the usability of 8 widely used ChIP-Seq web tools against 6 known usability quality metrics. Our study shows that the design of the studied ChIP-Seq web tools does not follow the UX design principles.
Co-Authors Asma’a Al-Mnayyis Farah Shatnawi Mahmoud Al-Ayyoub Mahmoud Hammad Mohammad Alsmirat Mohammad Alsmirat Mohammed Al-Smadi Saif Rawashdeh Saja Khaled Tawalbeh Sanaa Abu Alasal Sa’ad A. Al-Zboon Shadi AlZu’bi Wesam Alrashdan