Diabetic Retinopathy (DR) Severity Level Classification Using Multimodel Convolutional Neural Networks

Abidalkareem, Ali J.; Moaed, A.; Ibrahim, Ali K.; Zhuang, Hanqi; Altaher, Ali Salem; Ali, Ali Muhamed

doi:10.1109/embc44109.2020.9175606

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…The experimental results of the proposed method with two pre-trained models: VGG16 and GoogleNets. Illustrates that the sujested technique can achieve an accuracy of 93.2% by an ensemble of 10 random networks, compared to 81% obtained with transfer learning based on VGG19 [19].…”

Section: Introductionmentioning

confidence: 94%

Cardiovascular Patients Monitoring Using Internet of Things And Decision Tree

2022

IJCCCE

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The rapid growth of the human population and the spread of many bad habits affect the health of human beings, this leads to many health problems, such as heart disease, blood pressure, and diabetes. Some of these diseases require earlier detection and fast treatment, to avoid major risks, such as permanent damage or even life loss. The fast evolution of communications and nanotechnology nowadays is being facilitated to help with saving lives before great damage happens. This paper suggests a special architecture through which to monitor patients remotely using an Internet of Things (IoT)architecture for the purpose of notifying the paramedics and health care centers to rescue the life of cardiovascular patients. The experiments, after attempts with several types of classification algorithms, showed that The result shows that The decision tree has high accuracy for predicting the new heart attack and saving lives before happening of great damage .the result reached between( 83 to 87) percentage, which is a good percentage to avoid the risk of heart attacks and thus reducing death rates. Index Terms— decision tree, heart disease, health care,Internet of Things (IoT), sensors.

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Section: Introductionmentioning

confidence: 94%

Cardiovascular Patients Monitoring Using Internet of Things And Decision Tree

2022

IJCCCE

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“…For instance, metrics like mean and standard deviation were utilized [22]. A total of 66 features, spanning both frequency and time domains, were extracted to describe each activity window [23,24]. To derive features from the data, we utilized a window-based approach rather than relying on raw data, which would require classification for each individual data point [25].…”

Section: E Feature Extractionmentioning

confidence: 99%

Human Activity Recognition through Smartphone Inertial Sensors with ML Approach

Alanazi,

Aldahr,

Ilyas

2024

Eng. Technol. Appl. Sci. Res.

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Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period.

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“…Another kind of retinal illness is diabetic retinopathy, which is described as damage to the blood vessels in the retina caused by diabetes or high blood pressure. It is a progressive eye disease that affects a large proportion of working-age adults [4]. Lastly, CNV is caused by insufficient growth of blood vessels at the back of the eye.…”

Section: __________________________mentioning

confidence: 99%

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

Şahin

2022

Turkish Journal of Science and Technology

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The retina is a network layer that covers the back wall of the human eye and is made up of visual cells. The retina layer is the most complex and sensitive part of the eye, and disorders that affect it have a big impact on people's lives. The Optical Coherence Tomography (OCT) imaging technology can be used to diagnose diseases that arise because of pathological alterations in the retina. The importance of early diagnosis in the management of these illnesses cannot be overstated. In this article, a approach based on convolutional neural networks (CNN), a deep learning method, is presented for the detecting of retinal disorders from OCT images, based on the literature review. For disease detection and categorization, a CNN architecture is developed. The proposed method has been found to have a 94 percent accuracy rate in the identification of retinal disorders. In a deep learning application for classification, the proposed CNN network model is compared to the MobileNet50 network model available in the literature, and the results are provided. The evaluation parameter values for models trained using the 5-fold cross-validation approach for each type of disease in the retinal OCT images dataset are also included in the publication. The proposed method can clearly be utilized as a decision-making tool to assist clinicians in diagnosing retinal illnesses in a clinical context, based on its effectiveness thus far.

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Diabetic Retinopathy (DR) Severity Level Classification Using Multimodel Convolutional Neural Networks

Cited by 7 publications

References 9 publications

Cardiovascular Patients Monitoring Using Internet of Things And Decision Tree

Cardiovascular Patients Monitoring Using Internet of Things And Decision Tree

Human Activity Recognition through Smartphone Inertial Sensors with ML Approach

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

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