AI-Based Skin Disease Detection and Patient Engagement System

Abstract:

This project presents an AI-powered skin healthcare platform that detects various skin diseases using deep learning and offers real-time patient interaction through an intelligent chatbot. The system utilizes Convolutional Neural Networks (CNN) to classify skin conditions from image inputs and Natural Language Processing (NLP) models with Random Forest for chatbot-based patient support. The solution enables early diagnosis and digital guidance to enhance dermatological care accessibility and patient experience.

Introduction:

Skin diseases are among the most common health issues globally, often requiring expert visual inspection for diagnosis. Due to limited availability of dermatologists, delays in diagnosis can lead to worsening conditions. This project aims to automate skin disease identification using deep learning and engage users through a smart chatbot that can answer queries, explain diagnoses, and suggest preventive measures.

Problem Statement:

Many patients do not receive timely treatment for skin diseases due to:

• Lack of awareness
• Unavailability of dermatologists
• Hesitation to consult specialists in person

There is a need for an automated system that can provide instant insights and enable users to interact with a digital assistant for reliable dermatological guidance.

Existing System and Disadvantages:

Existing System:

• Manual diagnosis by skin specialists
• Static websites offering general skin care content
• Basic symptom-checker bots with no ML-based learning

Disadvantages:

• High dependency on human expertise
• Generic, non-personalized suggestions
• Inability to visually analyze and identify skin disease
• No intelligent real-time support

Proposed System and Advantages:

Proposed System:

• A CNN-based model for detecting and classifying skin diseases from uploaded images.
• A chatbot trained using NLP + Random Forest to handle patient queries related to symptoms, causes, remedies, and doctor recommendations.

Advantages:

• Instant, image-based skin disease detection
• Real-time patient interaction with intelligent feedback
• Greater accessibility for rural or remote users
• Personalized responses and suggestions
• Reduced burden on dermatology clinics

Modules:

1. Skin Disease Detection Module
   • Input: Uploaded image
   • Model: CNN
   • Output: Detected skin condition (e.g., Impetigo, Shingles)
2. Chatbot Assistance Module
   • Input: Natural language question
   • Model: TF-IDF + Random Forest
   • Output: Relevant answer based on training JSON
3. User Interface Module
   • Upload, preview, and interact with predictions and chatbot
   • Responsive HTML/CSS/JavaScript frontend
4. Admin Dashboard
   • View feedback
   • Manage submitted cases
5. Feedback Module
   • Allows users to share their experience and suggestions

Algorithms / Models Used:

• Convolutional Neural Network (CNN) for image-based skin disease classification
• TF-IDF + Random Forest Classifier for chatbot NLP processing
• ImageDataGenerator for augmentation and better training accuracy

Software and Hardware Requirements:

Software:

• Python 3.x
• Flask Web Framework
• TensorFlow / Keras
• Scikit-learn
• Jinja2 Templates
• MySQL for feedback storage

Hardware:

• Minimum: 4GB RAM, i3 processor
• Recommended: GPU for CNN training (NVIDIA GTX or above)
• Webcam or image upload capability on client side

Conclusion:

This system provides a scalable and accessible solution for early skin disease detection using AI. It combines image recognition through CNN with an intelligent NLP-based chatbot for a complete diagnostic and advisory experience. The system can reduce diagnostic delays and assist healthcare workers or patients in underserved regions.

Future Enhancements:

• Add voice-enabled chatbot functionality
• Integrate WhatsApp/Telegram bot for mobile use
• Expand disease dataset to cover more dermatological conditions
• Add multilingual chatbot response handling
• Connect to online dermatology appointment booking