Skinline AI is a groundbreaking deep learning project aimed at revolutionizing skin disease detection. By simply inputting an image, our innovative system can accurately detect various skin diseases, providing a swift and accurate diagnosis. Our model has been meticulously trained on a diverse dataset, enabling it to recognize 12 different diseases across 6 major classes.
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Model Architecture: Skinline AI is powered by a custom implementation based on the ResNet50 model, ensuring robust and accurate predictions.
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Dataset: We have curated a comprehensive dataset comprising approximately 1800 high-resolution images of skin diseases. These images cover 12 diseases categorized into 4 major classes:
- Bacterial: Cellulitis, Impetigo
- Cancerous: BCC Carcinoma, Melanoma
- Fungal: Athlete's Foot, Nail Fungus, Ringworm
- Inflammatory: Acne Vulgaris, Rosacea
- Parasitic: Cutaneous Larva Migrans
- Viral: Chicken Pox, Shingles
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Training Process: Through the use of libraries such as OpenCV and multiple epochs of selective training, we have achieved an impressive validation accuracy ranging between 85-90%.
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Integration: This model has been seamlessly integrated into a user-friendly website, designed to be a one-stop solution for individuals seeking information and diagnosis for skin diseases.
For those interested in exploring further:
While our model is already delivering exceptional accuracy, we recognize the potential for further improvements. Future plans include:
- Regular Updates: Stay tuned for regular updates and enhancements to the model.
- Data Augmentation: Exploring additional techniques to augment the dataset for improved accuracy.
- Scalability: Our model is designed to scale with ease, accommodating larger datasets and diverse diseases.
We are excited to share our journey in developing Skinline AI! If you have any questions, ideas for collaboration, or feedback, please feel free to reach out.
- 🔗 LinkedIn: Skinline AI
Let's redefine the future of skin disease diagnosis together! 🚀