Anomaly Detection Module

The Detection module automatically identifies suspicious regions in medical images, such as X-rays or CT scans.

---

🎯 Purpose

This module flags potential abnormalities to support radiologists in prioritizing cases and reducing oversight.

---

🧩 How It Works

• Uses state-of-the-art computer vision techniques to scan images.
• Applies convolutional neural networks (CNNs) trained on labeled datasets.
• Outputs bounding boxes and heatmaps highlighting areas of interest.

---

⚙️ Key Parameters

Parameter	Type	Description	Default
sensitivity	float	Controls detection threshold, 0.0 (low) to 1.0 (high)	0.75
image_modality	string	Type of input image: xray, ct, mri	xray
min_area	int	Minimum size in pixels for detected anomalies	50

tip

• Adjust sensitivity to balance false positives vs false negatives.
• Higher sensitivity catches more anomalies but increases noise.

---

📦 Output Format

Detection results include:

• bounding_boxes: List of rectangles around suspicious areas.
• heatmaps: Optional overlay images highlighting regions.
• confidence_scores: Probability estimates per detected anomaly.

{
  "detections": [
    {
      "bounding_box": [120, 80, 240, 160],
      "confidence_score": 0.92,
      "type": "nodule"
    }
  ]
}

---

🛠️ Usage Example

curl -X POST "https://api.neolens.ai/v1/detection" \
  -H "Authorization: Bearer <API_KEY>" \
  -F "image=@chest_xray.png" \
  -F "sensitivity=0.85"

---

🚩 Limitations

• May miss very small or subtle anomalies.
• False positives possible in noisy images.
• Performance varies by image modality and quality.