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Artificial Intelligence · Mini Project
Nail Disease Detection
Classifies common nail conditions from phone photographs
1-2 weeks build
Overview
A compact image classifier that sorts nail photos into conditions such as fungal infection, clubbing, pitting and healthy, trained on a public nail-disease image dataset. The kit focuses on transfer learning with a small dataset and heavy augmentation, and ships a Streamlit app for phone uploads.
What makes it stand up
- 01Public nail-disease image dataset with several condition classes
- 02Transfer learning with MobileNet for fast, light inference
- 03Augmentation tuned for lighting and angle variation in phone photos
- 04Per-class F1 and confusion matrix in the report
- 05Academic screening prototype, not a dermatology diagnosis
Module breakdown
- 01
Dataset
Cleaning, class balancing and train-val-test splits.
- 02
Augmentation
Rotation, brightness and crop transforms.
- 03
Model
MobileNet backbone with a fine-tuned classification head.
- 04
Evaluation
Metrics, misclassified examples and error analysis.
- 05
Demo app
Streamlit upload with top classes and a disclaimer.
After this, you will be able to
- Explain why transfer learning works on a small dataset
- Defend your augmentation choices with before-and-after results
- Demo predictions on photos taken during the viva
Technology stack
- Python
- TensorFlow
- Keras
- OpenCV
- Streamlit
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support