The Online Journal of Science and Technology, 2015
Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes... more Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes “death”. Therefore, it is a life-threatening condition and it should be detected before it occurs. Heart attack is so far predicted using the conventional ways of doctor’s examination and by performing some medical tests such as stress test, ECG, and heart CTScan etc. The coronary vessels constriction, the cholesterol levels in the arteries, and other attributes can be good indicators for making effective decisions. In this paper, a neural network based support decision system is developed for the prediction of heart attack. The proposed system uses 14 medical attributes, obtained from the Cleveland database such as sex, heart rate, and vessels narrowing etc. Two attributes have been emphasized in order to distinguish the heart attack from other heart diseases; the vessels constriction rate and the chest pain type. The testing results show high efficiency and capability for the designed...
This is the first dataset used in the paper "Deep Learning Based on Residual Networks for Au... more This is the first dataset used in the paper "Deep Learning Based on Residual Networks for Automatic Sorting of Bananas". It contains images of healthy and defective bananas. In case you would like to use this dataset please cite this article:Abdulkader Helwan et al., Deep Learning Based on Residual Networks for Automatic Sorting of Bananas", Journal of Food Quality, 2021.<br>
Iris tumors, so called intraocular tumors are kind of tumors that start in the iris; the colored ... more Iris tumors, so called intraocular tumors are kind of tumors that start in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris tumor detection system since the available techniques used currently are still not efficient. The combination of the image processing different techniques has a great efficiency for the diagnosis and detection of the iris tumor. Image processing techniques improve the diagnosis of the tumor by enhancing the quality of the images, so the physicians diagnose properly. Moreover, using some techniques such as edge detection and image fusion helps in detecting and segmenting the tumor, located in the iris. This paper aims to develop a detection system that automatically detects the presence of abnormalities or tumors in the iris. The suggested system combines different image processing techniques such as image filtering, images adding, canny edge detection, and image fusion. These methods are use...
2015 International Conference on Advances in Biomedical Engineering (ICABME), 2015
Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pu... more Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pupil. This growth is usually pigmented and non-cancerous, and therefore harmless; often requiring little medical attention. However, it has been established that there exists a relatively high risk of transformation of such growths into iris melanoma, which is cancerous or malignant. Furthermore, it has been shown that iris nevus infected patients also run risk of developing secondary glaucoma which requires very crucial medical intervention. Considering the above mentioned severe medical conditions that are associated with iris nevus, its diagnosis hence becomes very important. Generally, the diagnosis of iris nevus is achieved by examining eye images of patients taken by a medical expert. However, diagnosis is not an easily achievable task considering how racial and environmental factors affect the colour of patients' irises and pupils; hence pigmented growths may be concealed from a medical examiner. Also, factors such as stress and fatigue from examiners can lead to erroneous diagnosis. This research presents the use of trained hybrid auto encoders in the intelligent diagnosis of iris nevus. It is suggested that the use of the designed system as described in this work can significantly raise the confidence of medical diagnosis.
2015 International Conference on Advances in Biomedical Engineering (ICABME), 2015
Mortality rate increases all over the world on a daily basis. The reason for this could be largel... more Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient to avoid misdiagnosis. The dataset of UCI statlog heart disease has been used in this experiment. The dataset is comprises thirteen features which are vital in diagnosis of heart diseases. The system is model on a multilayer neural network trained with backpropagation and simulated on feedforward neural network. The recognition of 85% was obtained from testing of the network.
Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learnin... more Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learning different level image representations that helps in image classification in different fields. These networks have been trained on millions of images, so they gained a powerful ability of extracting the rightful features from input images, which results in accurate classification. In this research, we investigate the effects of transfer learning based convolutional neural networks for the iris tumor malignancy identification as it is notoriously hard to distinguish an iris nevus from an iris tumor. Features are transferred from a CNN trained on a source task, i.e. ImageNet, to a target task, i.e. iris tumor datasets. We transfer features learned from AlexNet and VGG-16 that are trained on ImageNet, to classify three different iris images types which are: iris nevus unaffected, iris cysts, and iris melanocytic tumors. The employed pre-trained models are modified by replacing their feedfo...
Supplemental material, sj-pdf-1-sci-10.1177_00368504211000889 for An online survey and review abo... more Supplemental material, sj-pdf-1-sci-10.1177_00368504211000889 for 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 by Caihua Ma, Lingxin Ma, Abdulkader Helwan, Mohammad Khaleel Sallam Ma'aitah, Sayed Abdulla Jami, Siam Al Mobarak, Niranta Kumar Das and Md Ariful Haque in Science Progress
This study presents the design of an intelligent system based on deep learning for grading fruits... more 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 sys...
To examine basic COVID-19 knowledge, coping style and exercise behavior among the public includin... more 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...
Computational and Mathematical Methods in Medicine, 2021
The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for... more 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 radiologist...
The Online Journal of Science and Technology, 2015
Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes... more Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes “death”. Therefore, it is a life-threatening condition and it should be detected before it occurs. Heart attack is so far predicted using the conventional ways of doctor’s examination and by performing some medical tests such as stress test, ECG, and heart CTScan etc. The coronary vessels constriction, the cholesterol levels in the arteries, and other attributes can be good indicators for making effective decisions. In this paper, a neural network based support decision system is developed for the prediction of heart attack. The proposed system uses 14 medical attributes, obtained from the Cleveland database such as sex, heart rate, and vessels narrowing etc. Two attributes have been emphasized in order to distinguish the heart attack from other heart diseases; the vessels constriction rate and the chest pain type. The testing results show high efficiency and capability for the designed...
This is the first dataset used in the paper "Deep Learning Based on Residual Networks for Au... more This is the first dataset used in the paper "Deep Learning Based on Residual Networks for Automatic Sorting of Bananas". It contains images of healthy and defective bananas. In case you would like to use this dataset please cite this article:Abdulkader Helwan et al., Deep Learning Based on Residual Networks for Automatic Sorting of Bananas", Journal of Food Quality, 2021.<br>
Iris tumors, so called intraocular tumors are kind of tumors that start in the iris; the colored ... more Iris tumors, so called intraocular tumors are kind of tumors that start in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris tumor detection system since the available techniques used currently are still not efficient. The combination of the image processing different techniques has a great efficiency for the diagnosis and detection of the iris tumor. Image processing techniques improve the diagnosis of the tumor by enhancing the quality of the images, so the physicians diagnose properly. Moreover, using some techniques such as edge detection and image fusion helps in detecting and segmenting the tumor, located in the iris. This paper aims to develop a detection system that automatically detects the presence of abnormalities or tumors in the iris. The suggested system combines different image processing techniques such as image filtering, images adding, canny edge detection, and image fusion. These methods are use...
2015 International Conference on Advances in Biomedical Engineering (ICABME), 2015
Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pu... more Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pupil. This growth is usually pigmented and non-cancerous, and therefore harmless; often requiring little medical attention. However, it has been established that there exists a relatively high risk of transformation of such growths into iris melanoma, which is cancerous or malignant. Furthermore, it has been shown that iris nevus infected patients also run risk of developing secondary glaucoma which requires very crucial medical intervention. Considering the above mentioned severe medical conditions that are associated with iris nevus, its diagnosis hence becomes very important. Generally, the diagnosis of iris nevus is achieved by examining eye images of patients taken by a medical expert. However, diagnosis is not an easily achievable task considering how racial and environmental factors affect the colour of patients' irises and pupils; hence pigmented growths may be concealed from a medical examiner. Also, factors such as stress and fatigue from examiners can lead to erroneous diagnosis. This research presents the use of trained hybrid auto encoders in the intelligent diagnosis of iris nevus. It is suggested that the use of the designed system as described in this work can significantly raise the confidence of medical diagnosis.
2015 International Conference on Advances in Biomedical Engineering (ICABME), 2015
Mortality rate increases all over the world on a daily basis. The reason for this could be largel... more Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient to avoid misdiagnosis. The dataset of UCI statlog heart disease has been used in this experiment. The dataset is comprises thirteen features which are vital in diagnosis of heart diseases. The system is model on a multilayer neural network trained with backpropagation and simulated on feedforward neural network. The recognition of 85% was obtained from testing of the network.
Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learnin... more Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learning different level image representations that helps in image classification in different fields. These networks have been trained on millions of images, so they gained a powerful ability of extracting the rightful features from input images, which results in accurate classification. In this research, we investigate the effects of transfer learning based convolutional neural networks for the iris tumor malignancy identification as it is notoriously hard to distinguish an iris nevus from an iris tumor. Features are transferred from a CNN trained on a source task, i.e. ImageNet, to a target task, i.e. iris tumor datasets. We transfer features learned from AlexNet and VGG-16 that are trained on ImageNet, to classify three different iris images types which are: iris nevus unaffected, iris cysts, and iris melanocytic tumors. The employed pre-trained models are modified by replacing their feedfo...
Supplemental material, sj-pdf-1-sci-10.1177_00368504211000889 for An online survey and review abo... more Supplemental material, sj-pdf-1-sci-10.1177_00368504211000889 for 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 by Caihua Ma, Lingxin Ma, Abdulkader Helwan, Mohammad Khaleel Sallam Ma'aitah, Sayed Abdulla Jami, Siam Al Mobarak, Niranta Kumar Das and Md Ariful Haque in Science Progress
This study presents the design of an intelligent system based on deep learning for grading fruits... more 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 sys...
To examine basic COVID-19 knowledge, coping style and exercise behavior among the public includin... more 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...
Computational and Mathematical Methods in Medicine, 2021
The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for... more 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 radiologist...
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