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Skin Disease Detection Final Year using Machine Learning

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Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

DermaSense is a final year project built with Python Flask, TensorFlow/Keras, and SQLite for students who want a machine learning based healthcare web application. This final year project allows users to upload skin images, run CNN inference, view predicted disease labels with confidence scores, and maintain private prediction history. The system also supports disease information, precautions, medicine suggestions, optional Grad-CAM overlays, printable reports, and user profile management. The admin console of this final year project includes user management, prediction logs, disease catalog management, precautions, medicines, model upload, model activation, monitoring, and maintenance tools. With support for .keras and .h5 models, DermNet-style class labels, and a demo model generator, DermaSense is suitable for a final year major project in machine learning, Flask, TensorFlow, image processing, or healthcare AI.

Technical snapshot

Project
Skin Disease Detection Final Year using Machine Learning
Stack
MACHINE-LEARNING
Includes
Code, DB, README
License
Academic submission
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Admin Features

Modules and controls available to administrators

  • Separate admin login for this final year project with distinct admin session
  • Admin dashboard with aggregate stats, recent predictions, and activity-style feed
  • User list, search, view, and delete features
  • Global prediction and uploaded image log
  • Disease CRUD with class order screen for model index alignment
  • Precaution and medicine suggestion CRUD per disease
  • Model management for .keras and .h5 uploads
  • Optional class label JSON upload or pasted JSON support
  • Input preprocessing mode selection: pixel255 or unit
  • Model validation before activation
  • Disease histograms and recent monitoring logs
  • Per-user activity summaries
  • Bulk clear tools for demo maintenance
  • Admin profile and password update

User Features

What end users can do in this application

  • User registration and login in this final year project with email or username
  • Dashboard with welcome section, analysis counts, metrics, and recent predictions
  • Profile update for name, email, username, phone, organization, and country
  • Password change support
  • Skin image upload with JPG/JPEG/PNG validation
  • Image upload size limit support
  • CNN inference using active TensorFlow/Keras model
  • Prediction result with disease label and confidence score
  • Disease precautions and medicine suggestions where available
  • Optional cause and treatment information
  • Optional Grad-CAM heatmap image
  • Case title and private notes
  • Prediction history with search and filter
  • Delete own predictions with confirmation
  • Printable HTML report and download support
  • Access control so users only see their own records

Other Features

Additional capabilities included in the project

  • SQLite database used by default in this final year project
  • Optional DATABASE_URL override for another database
  • Lightweight SQLite migrations for existing databases
  • Supports pretrained model.h5 workflow
  • Supports .keras demo model generation using create_model.py
  • 24-class DermNet-style class label workflow
  • dermnet_class_labels.json support for correct model output mapping
  • Optional Indonesian clinical blurbs import using scripts/import_dermnet_catalog.py
  • Optional OpenCV Grad-CAM support
  • Optional HDF5 support through h5py
  • Seed script for demo user and admin accounts
  • Suitable for final year project demo, viva, source code review, and report preparation
  • Educational decision-support disclaimer included

How to Run

Step-by-step setup on your laptop or PC

  • Open the final year project folder in terminal:
    cd "path/to/Skin Disease Detection using machine learning and tensorflow"
  • Create and activate a virtual environment:
    python -m venv .venv
    Windows: .venv\Scripts\activate
    Linux/macOS: source .venv/bin/activate
  • Install dependencies:
    pip install -r requirements.txt
  • Optional: initialize demo data and model:
    python seed.py
    python create_model.py
  • Optional: import DermNet catalog information:
    python scripts/import_dermnet_catalog.py
  • Start the final year Flask TensorFlow project:
    python run.py
  • Open the app at:
    http://127.0.0.1:5000/
  • Recommended model activation path:
    • Login as admin
    • Go to Admin → Model
    • Upload model.h5
    • Attach dermnet_class_labels.json
    • Choose “Scale to 0–1 (divide by 255)”
    • Submit “Validate & activate”

Login Credentials

Default demo accounts for testing after setup

Demo User

  • Username: demo_user
  • Password: UserPass123
  • URL: /auth/login

Administrator

  • Username: admin
  • Password: AdminPass123
  • URL: /auth/admin/login

License

Usage terms for academic and personal projects

Related Tags

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Skin Disease Detection Final Year using Machine Learning Source Code Final Year MACHINE-LEARNING Project Ready-to-Run Code With Database File Plagiarism-Free Faculty Approved skin disease detection final year project Flask TensorFlow project CNN skin disease classification Python skin disease detection system machine learning healthcare project skin image prediction source code DermaSense project Grad-CAM skin disease project TensorFlow Keras model management final year ML project

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