International Research Journal of Education and Technology

The rapid advancement of Artificial Intelligence (AI) in healthcare has enabled the development of intelligent systems capable of assisting in early diagnosis and decision-making. This paper presents an Intelligent Medical Chatbot System using Natural Language Processing (NLP) and VGG16 for Symptom Analysis and Skin Disease Prediction. The proposed system integrates a conversational chatbot framework with deep learning-based image classification to provide preliminary medical guidance.

The chatbot module leverages Natural Language Processing techniques to understand user-input symptoms in natural language and map them to potential medical conditions using machine learning-based classification. For dermatological analysis, the system employs the VGG16 convolutional neural network model for skin disease prediction from uploaded images. The VGG16 model is fine-tuned using transfer learning on a labeled skin disease dataset to improve accuracy and reduce training time.

The hybrid architecture enables both text-based symptom analysis and image-based skin disease detection within a unified platform. Experimental results demonstrate improved classification accuracy, reliable symptom interpretation, and efficient response generation. The system aims to assist users with preliminary diagnosis, reduce hospital workload, and promote accessible healthcare services, especially in remote areas.

This research highlights the potential of combining NLP-driven conversational agents with deep learning-based computer vision models to build scalable, intelligent, and user-friendly digital healthcare solutions.