Deep Learning for Early-Stage Cataract Detection
Cataracts are a leading cause of vision impairment globally. This project leverages Computer Vision to provide an automated screening tool that can identify cataracts from digital ocular images. By utilizing a Convolutional Neural Network (CNN), the model classifies images into "Cataract" and "Normal" categories, aiming to bridge the gap in accessible eye care through rapid AI diagnostics.
Ocular images often contain noise or varying lighting conditions. The pipeline includes:
- Grayscale Conversion & Normalization: Standardizing pixel values for model consistency.
- Gaussian Blurring: Reducing noise to focus the CNN on significant structural anomalies in the lens.
- Data Augmentation: Using random rotations and zooms to help the model generalize across different camera angles and patient positions.
The model architecture is designed to capture fine-grained textures in the eye:
- Feature Extraction: Multiple Convolutional layers to identify clouding patterns in the lens.
- Pooling: Dimensionality reduction to ensure the model remains computationally efficient.
- Dense Layers: Fully connected layers with a Dropout rate of 0.5 to prevent overfitting on the training set.
- Activation: Sigmoid output for precise binary classification.
In medical AI, "Accuracy" isn't enough; we focus on reliability:
- Sensitivity (Recall): Ensuring we minimize "False Negatives" (missing a cataract).
- Specificity: Ensuring healthy eyes are not misclassified.
- Confusion Matrix: Providing a transparent view of the model's diagnostic performance.
- Deep Learning: TensorFlow / Keras
- Image Processing: OpenCV, PIL
- Data Analysis: NumPy, Pandas
- Visualization: Matplotlib, Seaborn
This project is for educational and research purposes only. It is intended to assist medical professionals, not to replace professional clinical diagnosis.
Maintained by DHARKIVE-STUDIO