Skin cancer is one of the most common cancers globally, but early detection can significantly improve the chances of successful treatment. Unfortunately, many people fail to identify early symptoms due to a lack of awareness or limited access to dermatologists. This project proposes developing an AI-based mobile application that can detect early signs of skin cancer through image analysis of skin lesions, using deep learning technology.
Problem Statement:
The delay in identifying skin cancer often leads to severe health consequences. There is a need for an affordable, accurate, and user-friendly mobile tool that can help in early detection and encourage users to consult medical professionals promptly.
Objectives:
- To collect and process a dataset of skin lesion images.
- To train an AI model capable of classifying benign and malignant lesions.
- To develop a mobile app that allows users to scan or upload skin images.
- To test the app’s accuracy and performance using real-world data.
Methodology:
The project will follow a systematic approach divided into five main stages:
| Step No. | Process | Description | Tools/Software | Example Results (with Numbers) |
|---|---|---|---|---|
| 1 | Data Collection | 10,000 skin lesion images (7,000 benign, 3,000 malignant) were collected from the ISIC (International Skin Imaging Collaboration) database. | ISIC Archive, Python | Total Images: XXXXX |
| 2 | Data Preprocessing | Images resized to 224×224 pixels, labeled, normalized, and augmented to improve model training. | OpenCV, NumPy | Processed Images: XXXX |
| 3 | Model Training | A CNN (Convolutional Neural Network) model was trained using 80% of the dataset, validated with 10%, and tested with 10%. | TensorFlow, Keras | Training Accuracy: XXX Validation Accuracy: XXX |
| 4 | App Development | Integration of the trained AI model into a mobile app that accepts image input and provides results. | Android Studio, Flutter, TensorFlow Lite | Average Prediction Time: X seconds |
| 5 | Testing & Validation | Compared AI results with dermatologist evaluations for 500 test images. | Confusion Matrix, Accuracy Metrics | Accuracy: XX Precision: XX Recall: XX |
Expected Outcomes:
- A functional AI-based mobile app capable of detecting early signs of skin cancer with over 90% accuracy.
- Faster, accessible, and cost-effective preliminary diagnosis for users.
- Increased awareness of skin health and prevention.
Conclusion:
The proposed AI-based mobile app will be a valuable tool for early skin cancer detection. It will combine artificial intelligence with mobile technology to promote public health, enable self-screening, and encourage users to seek timely medical advice.
