An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

Area of Science:

• Dermatology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Skin disease diagnosis presents significant challenges globally, particularly in detection and management.
• Traditional data processing methods struggle with the large, complex health data associated with skin conditions.
• Accurate and efficient diagnostic tools are crucial for effective dermatological care.

Purpose of the Study:

• To develop an advanced model for simplifying the processing of complex skin health data.
• To leverage nanotechnology and neural networks for improved skin disease classification.
• To accurately predict specific skin diseases including melanoma, nevus, psoriasis, and seborrheic keratosis.

Main Methods:

• Utilized nanotechnology sensors to acquire detailed skin condition data (chemical, physical, biological).
• Employed multilayered perceptron with backpropagation neural networks (MLP-BPNN) for data analysis and prediction.
• Focused on enhancing detection specificity, sensitivity, and multiplexing ability for optimal image classification.

Main Results:

• The proposed model effectively processes complex skin data using MLP-BPNN.
• Nanotechnology sensors provide high-quality images for classification.
• The MLP-BPNN technique demonstrates efficacy in predicting various skin diseases.

Conclusions:

• The integration of nanotechnology sensors and MLP-BPNN offers a powerful solution for skin disease diagnosis.
• This approach simplifies complex data processing, leading to more accurate predictions.
• The model shows promise for early and precise identification of common skin conditions.