- Data Collection and Training
AI is trained on thousands of pre-annotated images that contain both normal and pathological data. This allows the system to learn and recognize the characteristic features associated with cancerous tumors.
2. Medical Image Analysis- Image Preprocessing: AI enhances image quality, removes noise, and increases contrast.
- Feature Extraction: AI analyzes tissue structures and density to identify anomalies such as nodules or shadows indicating tumors.
3. Anomaly Classification- Contour and Texture Analysis: AI determines the shape, boundaries, and density of formations to differentiate malignant tumors from benign ones.
- Structural Analysis: Based on CT and MRI data, AI assesses the likelihood of malignancy.
4. Comparison with Reference Data- AI evaluates the probability of malignancy (e.g., "85% likelihood of malignancy"). It highlights suspicious areas, helping the doctor focus on critical details.
5. Result Output- AI evaluates the probability of malignancy (e.g., "85% likelihood of malignancy"). It highlights suspicious areas, helping the doctor focus on critical details.
6. Feedback and Self-Learning- The system continuously improves by receiving feedback from doctors. This allows the AI to enhance its accuracy and efficiency with each new case.