محمد خليل المعايطة

ASUجامعة العلوم التطبيقية الخاصة - الصفحة الرئيسيةكلية الهندسة والتكنولوجيامحمد خليل المعايطةالأبحاث

الأبحاث

Deep Convolutional Neural Networks for Chest Diseases Detection

Authors : Rahib H. Abiyev, Mohammad Khaleel Sallam Ma’aitah

Abstract :Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

Keywords :

Visit

Intelligent Classification of Liver Disorder using Fuzzy Neural System

Authors : Mohammad Khaleel Sallam Ma’aitah, Rahib Abiyev, and Idoko John Bush

Abstract :In this study, designed an intelligent model for liver disorders based on Fuzzy Neural System (FNS) models is considered. For this purpose, fuzzy system and neural networks (FNS) are explored for the detection of liver disorders. The structure and learning algorithm of the FNS are described. In this study, we utilized dataset extracted from a renowned machine learning data base (UCI) repository. 10 folds crossvalidation approach was explored for the design of the system. The designed algorithm is accurate, reliable and faster as compared to other traditional diagnostic systems. We highly recommend this framework as a specialized training tool for medical practitioners.

Keywords : Artificial neural networks; fuzzy systems; fuzzy neural systems; liver disorders

Visit

Integrated artificial intelligence algorithm for skin detection

Authors : Idoko John Bush, Rahib Abiyev, Mohammad Khaleel Sallam Ma’aitah and Hamit Altıparmak

Abstract :The detection of skin colour has been a useful and renowned technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as used in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM) clustering rule, clustered the data and for each cluster/class a rule is generated. Finally, corresponding output from data mapping of pseudo-polynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS).

Keywords :

Visit

Radiologists versus deep convolutional neural networks: a comparative study for diagnosing COVID-19

Authors : Mohammad Khaleel Sallam Ma’aitah,Hani Hamdan,Dilber Uzun Ozsahin,and Ozum Tuncyurek

Abstract : The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.

Keywords :

Visit

Deep learning based on residual networks for automatic sorting of bananas

Authors :Abdulkader Helwan, Mohammad Khaleel Sallam Ma’aitah, Rahib H Abiyev, Selin Uzelaltinbulat, Bengi Sonyel

Abstract : This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits.

Keywords :

Visit

An online survey and review about the awareness, coping style, and exercise behavior during the “COVID-19 pandemic situation” by implementing the cloud-based medical treatment technology system in China among the public

Authors :Caihua Ma, Lingxin *, Abdulkader Helwan, Mohammad Khaleel Sallam Ma’aitah, Sayed Abdulla Jami, Siam Al Mobarak, Niranta Kumar Das, and Md Ariful Haque

Abstract : To examine basic COVID-19 knowledge, coping style and exercise behavior among the public including government-provided medical cloud system treatment app based on the internet during the outbreak. Besides, to provide references for developing targeted strategies and measures on prevention and control of COVID-19. We conducted an online survey from 11th to 15th March 2020 via WeChat App using a designed questionnaire. As well as aim to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Valid information was collected from 1893 responders (47.07% males and 52.93% females aged 18–80 years, with a mean age of 31.05 ± 9.86) in 20 provincial-level regions across China. From the responders, 92.90% and 34.81% were scaled pass and good and above scores for the knowledge about the novel coronavirus epidemic. 38.44% were scaled poor scores and only 5.40% were scaled good and above scores for appropriate behavior coping with the pandemic. Among the responders, 52.14% reported having active physical exercise in various places during the previous 1 week. For all the responders, appropriate behavior coping correlated positively with physical exercise (p < 0.05); the daily consumed time for getting the epidemic-related information correlated positively with the score for cognition on the epidemic’s prevention measures (r = 0.111, p < 0.01) and on general knowledge about the epidemic (r = 0.087, p < 0.01). Targeted and multiple measures for guidance on the control of COVID-19 among the public should be promoted to improve the cognition on basic knowledge, behaviors and treatment.

Keywords : COVID-19, nCapp, coping style, exercise behavior, online survey

Visit

اتصل بنا

د. محمد خليل المعايطة

آخر الأخبار

22
كانون الثاني

مسابقة الروبوت الماشي

19
كانون الثاني

كلية الهندسة تنظّم النسخة الأولى من مسابقة الوعي بالطاقة المستدامة

6
كانون الثاني

ورشة عمل بعنوان التهديدات السيبرانية الناشئة والحالة الراهنة لأمن المعلومات