AI/ML

AI-Powered PCOS Detection

This research-focused project tackles the challenge of early PCOS diagnosis. Polycystic Ovary Syndrome is often difficult to diagnose consistently from ultrasound images due to noise and varying image quality. This system uses advanced deep learning to segment, detect, and classify ovarian cysts automatically.

Project typeIndependent build
StatusCompleted project
FocusAI/ML

Technology

U-NetYOLOGANPythonOpenCVMedical Imaging

System approach

  • Preprocessing Pipeline: Image augmentation and noise reduction using GANs.
  • Segmentation: U-Net architecture to isolate the region of interest (ovaries).
  • Detection: YOLOv8 for precise bounding box detection of cysts.

Challenges

  • Scarcity of annotated medical datasets for PCOS.
  • High variability in ultrasound image quality and equipment.
  • The need for highly interpretable results for medical professionals.

Solutions

  • Utilized Generative Adversarial Networks (GANs) to synthesize realistic ultrasound images, expanding the training dataset.
  • Implemented aggressive data augmentation (rotation, scaling, noise injection) to make the model robust to different ultrasound machines.
  • Added Grad-CAM visualizations to highlight the specific regions the model focused on, aiding doctor interpretability.

Key outcomes

01

Deep learning medical diagnostics

02

Automated ultrasound analysis

03

Enhanced diagnostic consistency