Skin Burn Image Data Flask in Python Projects

Skin Burn Image Classification and Severity Detection Using Deep Learning with Flask

Abstract

The Skin Burn Image Classification System is a web-based application developed using Python, Flask, Deep Learning, and Computer Vision to identify the severity of skin burns from uploaded images. The system classifies burn injuries into different categories (First-Degree, Second-Degree, Third-Degree, and Normal Skin) using a trained Convolutional Neural Network (CNN). The Flask framework provides a user-friendly interface where users can upload burn images and instantly receive prediction results. This project can be useful for educational purposes, preliminary screening, and research. It is not intended to replace professional medical diagnosis.

Existing System

Traditional burn diagnosis mainly depends on:

Manual visual inspection by healthcare professionals.
Subjective assessment based on experience.
Time-consuming diagnosis.
High possibility of human error for inexperienced users.
Lack of automated online prediction systems.
Disadvantages
Requires expert consultation.
Not suitable for remote screening.
Inconsistent diagnosis.
Slower response in emergency situations.
Difficult for non-medical users.
Proposed System

The proposed system uses Deep Learning (CNN) to automatically classify burn severity from skin images.

Features
Upload burn image.
Image preprocessing.
CNN-based feature extraction.
Burn severity prediction.
Confidence score.
Result visualization.
Flask web interface.
Prediction history stored in SQLite/MySQL.
Objectives
Detect skin burns automatically.
Classify burn severity.
Reduce diagnosis time.
Improve prediction accuracy.
Provide an easy-to-use web application.
Assist medical screening.

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