Deep Learning Approaches for Prognosis of Automated Skin Disease.

Area of Science:

• Dermatology
• Artificial Intelligence
• Medical Imaging

Background:

• Skin disorders are a global public health concern, exacerbated by environmental factors and lifestyle choices.
• Current diagnostic methods, often relying on invasive biopsies, present challenges due to variations in skin tones and the need for expert assessment.
• Early detection of skin conditions is frequently overlooked, leading to potential complications and delayed treatment.

Purpose of the Study:

• To develop an accurate and efficient deep learning-based system for classifying skin diseases.
• To overcome the limitations of manual assessment by creating an automated diagnostic framework.
• To enhance the precision of skin disease forecasting through advanced computational methods.

Main Methods:

• Implementation of a hybrid deep learning model combining MobileNetV2 for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis.
• Training the model on diverse datasets to distinguish between skin and non-skin tissues for accurate disease identification.
• Focus on achieving high accuracy in skin disease prediction while maintaining computational efficiency.

Main Results:

• The developed system demonstrates promising accuracy in classifying various skin conditions.
• The hybrid MobileNetV2-LSTM architecture effectively processes complex visual data for dermatological analysis.
• The model shows potential for efficient storage of state information, crucial for precise forecasting.

Conclusions:

• Deep learning frameworks, specifically the MobileNetV2-LSTM hybrid, offer a viable solution for automated skin disease classification.
• This approach can aid in early and accurate diagnosis, potentially reducing the burden on healthcare systems.
• Further research can explore integrating this technology into clinical practice for improved patient outcomes.