Artificial Intelligence Based Skin Classification Using GMM

Area of Science:

• Medical Informatics
• Computer Vision
• Dermatology

Background:

• Dermatological conditions such as measles, German measles, and chickenpox often present with similar symptoms like redness and rash.
• Accurate diagnosis of these skin diseases can be challenging and time-consuming due to symptom overlap and the need to analyze extensive patient data.

Purpose of the Study:

• To develop and evaluate a computer-aided diagnostic system utilizing Artificial Neural Networks (ANNs).
• To improve the speed and accuracy of diagnosing skin conditions by analyzing visual patterns and symptoms.

Main Methods:

• The study employed image processing techniques to extract relevant features from skin images.
• An Artificial Neural Network (ANN) classifier was trained to recognize patterns associated with specific dermatological diseases.
• The system processed image data, extracted characteristics, and categorized information for disease recognition.

Main Results:

• The developed ANN system demonstrated the potential for faster prognosis and recognition of skin diseases compared to human diagnosis.
• The system effectively analyzed symptom patterns for improved disease identification.

Conclusions:

• Artificial Neural Networks offer a promising tool for computer-aided diagnosis in dermatology, potentially enhancing diagnostic efficiency.
• This AI-driven approach can assist clinicians in making quicker and more accurate treatment decisions for skin conditions.