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.
Technology Stack
System Architecture
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.
The 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.
The 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 Results & Metrics
Deep learning medical diagnostics
Automated ultrasound analysis
Enhanced diagnostic consistency