Biosignal Processing & Medical Imaging

Công cụ cần thiết:

• MIT-BIH Arrhythmia Database
• Python với scikit-learn, scipy libraries
• ECG signal processing algorithms
• Machine learning classification models

Các bước thực hiện:

1. 1Preprocess ECG signals: filtering, baseline correction, noise removal
2. 2Extract features: R-peak detection, heart rate variability, morphological features
3. 3Implement classification algorithms: SVM, Random Forest, Neural Networks
4. 4Train models trên labeled arrhythmia dataset với cross-validation
5. 5Evaluate performance: sensitivity, specificity, accuracy metrics
6. 6Deploy real-time detection system với clinical validation

Kết quả mong đợi:

Arrhythmia detection system với >95% accuracy trên standard test datasets

Công cụ cần thiết:

• BraTS dataset (Brain Tumor Segmentation)
• TensorFlow/PyTorch framework
• U-Net architecture implementations
• Medical image visualization tools

Các bước thực hiện:

1. 1Preprocess MRI data: normalization, augmentation, format conversion
2. 2Implement U-Net architecture với attention mechanisms
3. 3Train segmentation model với multi-class tumor labeling
4. 4Apply post-processing techniques: morphological operations, connected components
5. 5Validate results với radiologist annotations using Dice coefficient
6. 6Create clinical visualization tools cho treatment planning

Kết quả mong đợi:

Brain tumor segmentation model với Dice score >0.85 cho enhancing tumor regions