AI cellverse 25

Team: technovators

Skin Condition Detection AI

CNN model developed by "Team Technovators"

It has an accuracy of over 75% with an ability to classify skin dieses based on the images provided by the users and provide a detailed solution roadmap to cure the skin condition.

🌟 Overview

This AI-powered project uses deep learning to classify skin conditions from medical images. Built with a Convolutional Neural Network (CNN), the model learns from thousands of labeled skin images to predict diseases accurately.

📂 Dataset: HAM10000

We use the HAM10000 dataset, which contains over 10,000 skin images across different categories, including:

• Melanoma (Skin cancer)
• Nevus (Moles)
• Basal cell carcinoma (BCC)
• Actinic keratosis (AK)
• Benign keratosis (BK)
• Dermatofibroma (DF)
• Vascular lesions

🔧 Preprocessing the Images

Before training the model, the images go through:

• Resizing – Standardizing image size for consistency 📏
• Normalization – Converting pixel values from 0–255 to 0–1 🎨
• Data Augmentation – Flipping, rotating, and zooming to improve model robustness 🔄

🏗️ Model Architecture (CNN)

The model follows a CNN-based approach, which mimics how the human brain recognizes patterns.

🚀 Layers of the CNN:

• Convolution Layers – Detects edges, textures, and color variations 🖼️
• Pooling Layers – Reduces image size while keeping important features 📉
• Dense Layers – Processes information and predicts the final skin condition 🧠

🎯 Model Training

The model is trained using supervised learning with:

• Loss Function: Categorical Crossentropy
• Optimizer: Adam (to adjust learning rates efficiently) ⚡
• Evaluation Metrics: Accuracy, Precision, Recall, and F1-Score 📊

Example Learning Process:

👶 Child learning to identify apples: At first, they confuse apples with tomatoes 🍎🍅, but with feedback, they improve. 🧠 Similarly, our AI refines its predictions over time through backpropagation.

📊 Model Evaluation

To measure performance, we use:

• Accuracy: Overall correctness ✅
• Confusion Matrix: Tracks classification errors 📉
• Precision & Recall: Balances between false positives & false negatives ⚖️

🔍 Making Predictions

Once trained, the model can analyze new skin images and classify the condition. 💡 Example: Just like Google Lens identifies objects, this AI can detect skin diseases! 📸

💡 Future Enhancements

🔹 Improve accuracy with more diverse datasets 📈
🔹 Real-time skin analysis via smartphone cameras 📷
🔹 AI-powered dermatology assistant for doctors 🏥

🤝 Contributors

👨‍💻 Developed by Technovators Team
📌 Inspired by the need for accessible skin disease detection 🔬

⚡ Empowering healthcare through AI! ⚡