Jaundiced & Normal Eyes Dataset

About Dataset

The images in this Jaundiced & Normal Eyes Dataset have been collected from Google Images and include a variety of angles, lighting conditions, and eye shapes to ensure broad applicability across different computer vision models. Whether you are exploring the early detection of jaundice or developing diagnostic tools for automated medical assistance, this dataset provides an excellent starting point.

With the growing interest in AI-driven healthcare solutions, this dataset can be utilized for:

• Classification: Differentiating between jaundiced and normal eyes using machine learning models.
• Object Detection: Detecting jaundice in eye images to support diagnostic applications.
• Segmentation: Isolating specific regions of the eye for more precise medical analysis, such as focusing on the sclera or iris.

By leveraging this dataset, users can delve into the intersection of computer vision and healthcare, building models that can potentially automate the detection of jaundice—a symptom commonly associated with liver conditions such as hepatitis, cirrhosis, or other related diseases. Early identification through AI can significantly assist medical professionals in monitoring and diagnosing such conditions.

Dataset Composition:

• Images Count: 100+ images (both jaundiced and non-jaundiced eyes).
• Image Types: JPG, PNG formats.
• Resolution: Varies across the dataset but maintains a standard quality suitable for most AI training applications.

Applications:

• Medical image classification and analysis.
• Training models for early detection of liver-related diseases.
• Research and development of AI tools for ophthalmology and healthcare diagnostics.
• Educational purposes to learn computer vision techniques in a real-world medical application.

This dataset is a perfect starting point for beginners in computer vision looking to apply techniques such as image processing, feature extraction, and machine learning, while also appealing to advanced users seeking to enhance AI healthcare solutions.

Potential Use Cases:

• Creating AI systems for hospitals to flag potential jaundice cases in patients.
• Developing mobile applications that can quickly assess eye health through photos.
• Training neural networks to classify and diagnose eye-related health issues.

Key Benefits:

• A practical dataset that introduces users to medical applications of AI.
• Easy to use for educational purposes and research.
• Offers a valuable resource for the healthcare industry to improve diagnostic accuracy using AI.