Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network

Nasser, Ekhlas S.; Dawood, Faten Abd Ali

doi:10.24996/ijs.2021.62.10.33

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…-Sensitivity or Recall: measures how frequently good occurrences are categorized [34]. Recall=𝑇𝑃/(𝑇𝑃 + 𝐹𝑁)…”

Section: Class Nomentioning

confidence: 99%

Binary Classification of Diabetic Retinopathy Using CNN Architecture

Khudaier,

Radhi

2024

Iraqi Journal of Science

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Diabetes mellitus (DM), a chronic, clinically heterogeneous condition, is becoming increasingly common all over the world. Insulin deficiency, resistance to insulin's actions on the body's surface, or both may lead to pancreatic beta-cell degeneration. Diabetes makes people more prone to its consequences, the most prevalent of which is diabetic retinopathy (DR). Deep learning algorithms surpass traditional classification models for illness diagnosis on photos of medical problems. Deep transfer learning models for medical DR detection were evaluated using the APTOS 2019 dataset. Deep transfer learning algorithms for medical diabetic retinopathy (DR) detection are being evaluated. MobileNet Convolutional Neural Networks (CNN) architecture is used to detect the DR in binary class classification tasks, which leverages pre-trained weights collected during the training process using the ImageNet database. Cohen Kappa, F1 score, recall, accuracy, and precision are some of the performance indicators used. According to the data, the given model is the most effective in terms of accuracy and training time for handling our challenges. Overall, MobileNet is a good pick. The following metrics were found to be accurate: 0.9455, precise: 0.94651, recall: 0.9455, F1 score: 0.94556, and Cohen Kappa score: 0.89083. This method might aid medical personnel in the early detection of diabetic retinopathy.

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“…-Sensitivity or Recall: measures how frequently good occurrences are categorized [34]. Recall=𝑇𝑃/(𝑇𝑃 + 𝐹𝑁)…”

Section: Class Nomentioning

confidence: 99%

Binary Classification of Diabetic Retinopathy Using CNN Architecture

Khudaier,

Radhi

2024

Iraqi Journal of Science

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“…• Sensitivity or Recall: Sensitivity is a measure of how many positive occurrences are favorably categorized [27].…”

Section: Performance Metricsmentioning

confidence: 99%

Diabetes Diagnosis Using Deep Learning

Khudair,

Radhi

2024

Iraqi Journal of Science

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Hyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data augmentation methods to enhance the dataset photographs, and balance the dataset (which was quite uneven), to avoid overfitting. By using Transfer learning we save training time by using a previously learned deep CNN and transfer learning weights. In this research, EfficientNetB1 is compared against Xception, InceptionV3, MobileNet, and ResNet50 as a deep transfer learning model. The proposed model's accuracy, precision, recall, and f1-score are all examined. The EfficientNetB1 model outperforms all others in terms of overall testing accuracy (86.1%), sensitivity (87.24%), precision (97.6%), and F1-Score (89.32 percent). This approach might help physicians diagnose Diabetic Retinopathy earlier.

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“…The ISCX VPN-NonVPN traffic dataset has been used [30][31][32][33][34][35][36]. The dataset includes two scenarios (A and B).…”

Section: Practical Framework: Datasetmentioning

confidence: 99%

Network Traffic Prediction Based on Boosting Learning

Kashmoola

Ahmed

Alsaleem

2022

eijs

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Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traffic patterns that can be categorized based on statistical characteristics. These methods help determine the type of traffic and protect user privacy at the same time. To classify encrypted traffic from end to end, this paper proposes using (XGboost) algorithms, finding the highest parameters using Bayesian optimization, and comparing the proposed model with machine learning algorithms (Nearest Neighbor, Logistic Regression, Decision Trees, Naive Bayes, Multilayer Neural Networks) to classify traffic from end to end. Network traffic has two classifications: whether the traffic is encrypted or not, and the target application. The research results showed the possibility of classifying dual and multiple traffic with high accuracy. The proposed model has a higher classification accuracy than the other models, and finding the optimal parameters increases the model accuracy.

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Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network

Cited by 6 publications

References 12 publications

Binary Classification of Diabetic Retinopathy Using CNN Architecture

Binary Classification of Diabetic Retinopathy Using CNN Architecture

Diabetes Diagnosis Using Deep Learning

Network Traffic Prediction Based on Boosting Learning

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