Assessfy Pvt. Ltd Moderate 4 milestones 49 marks

Skin Disease Detection System Using CNN

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Skin Disease Detection System Using CNN

Objective: Design a convolutional neural network (CNN) system to detect and classify skin diseases from images.

Context: In India, timely and accurate skin disease diagnosis is crucial, especially in rural areas with limited access to dermatologists.

What you'll build: You'll prepare an image dataset, develop and train a CNN using TensorFlow/Keras, and evaluate its precision and recall. The final model will be integrated with a simple GUI for user-friendly disease prediction from uploaded images.

Deliverables: Deliver a trained CNN model, evaluation metrics, and a functional GUI application for skin disease detection.

Milestones

1. Image dataset preparation

10 marks 7d

Collect a labeled image dataset representing various skin diseases, ensuring at least 500 images per class. Organize the images into train, validation, and test folders, and submit a summary table detailing class distribution and data sources. Verify that all images are clear, correctly labeled, and suitable for CNN input.

2. Model training

10 marks 7d

Build and train a convolutional neural network using your prepared dataset. Submit your model architecture, training logs, and final weights. Ensure your training achieves at least 80% accuracy on the validation set and document any data augmentation or preprocessing steps used.

3. Evaluation (precision/recall)

15 marks 7d

Evaluate your trained model on the test set, calculating precision and recall for each disease class. Submit a confusion matrix and a brief analysis of the results, highlighting strengths and weaknesses. Ensure your metrics are clearly reported and reproducible from your code.

4. Integration with GUI

14 marks 9d

Integrate your trained CNN model into a graphical user interface that allows users to upload an image and receive a predicted disease label. Submit the GUI code and a short demo video showing the full prediction workflow. Confirm that the interface is responsive and displays results clearly.

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Upcoming sessions

Session	Window	Enrolled
Skin Disease Detection System Using CNN	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

CNN architectureImage classificationDeep learning

Tools used

Python TensorFlow/Keras OpenCV

Prerequisites

Image datasets CNN basics

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

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Skin Disease Detection System Using CNN

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