Tuberculosis - Model Training Software
Tuberculosis (TB) model training software is an application or system designed to build data-based prediction models, using machine learning (ML) or artificial intelligence (AI) technology. This software helps researchers or health practitioners analyze data to detect, diagnose, monitor, or predict tuberculosis cases more effectively. Here is a detailed explanation:
Advantages of INDOTELEMED TB Model Training
Benefits of Model Training Software for TB
- Diagnostic Efficiency: Reduces the time required for diagnosis.
- Result Accuracy: Increases the accuracy of diagnosis compared to conventional methods.
- Accessibility: Provides automated diagnostic solutions in remote areas.
- Treatment Personalization: Helps design a customized treatment plan for patients.
Model Training Objectives for Tuberculosis Early TB Detection:
Build a model to detect early signs of TB from clinical data or radiological images such as chest X-rays.Help speed up diagnosis compared to manual methods.
Risk Prediction: Predict the likelihood of a person developing TB based on risk factors such as age, medical history, immune status, and geographic patterns.
Monitoring and Treatment: Monitor a patient’s progress in TB treatment, for example, by analyzing biomarkers or drug resistance patterns.
Prevention of Spread:Help health authorities predict the spread of TB in the community through analysis of epidemiological data.
Architecture Model Selection:
Architecture Models:
- Simple CNN
- MobileNetV2
- ResNet50
- VGG16
- Xception
- InceptionV3
- EfficientNet80
Completed with:
Epoch Setting
Batch Size
Learning Rate
Optimizer Selection
Data Augmentation
Confusion Matrix and ROC Curve
Training & Validation Graph
Epoch Setting
Batch Size
Learning Rate
Optimizer Selection
Data Augmentation
Confusion Matrix and ROC Curve
Training & Validation Graph
