Automated skin lesion assessment using mobile technologies and cloud platforms.

Area of Science:

• Dermatology
• Medical Imaging
• Mobile Health (mHealth)

Background:

• Skin cancer, particularly melanoma, poses a significant public health concern.
• Early detection of skin lesions is crucial for effective treatment and improved patient outcomes.
• Existing diagnostic methods can be resource-intensive and may not be readily accessible to all individuals.

Purpose of the Study:

• To develop and evaluate a smartphone-based system for the self-assessment of skin lesions.
• To create a mobile application capable of acquiring, identifying, and classifying skin moles.
• To leverage cloud infrastructure for enhanced computational and storage capabilities in lesion analysis.

Main Methods:

• Development of a mobile application for capturing and analyzing skin lesion images.
• Implementation of an image recognition algorithm to identify and classify moles (melanoma, nevus, benign).
• Utilization of a cloud-based architecture for data storage, processing, and model enhancement.

Main Results:

• The system successfully acquires and stores digital images of skin lesions.
• The mobile application demonstrates capability in identifying and classifying moles based on severity.
• Initial evaluations show promising results for the system's utility in preliminary skin lesion assessment.

Conclusions:

• The developed smartphone system offers a promising tool for the initial self-assessment of skin lesions.
• The integration of mobile technology and cloud computing facilitates accessible and flexible dermatological analysis.
• Further development and validation are warranted to establish the system's clinical efficacy.