Automated melanoma prevention & detection solution

Problem statement

Skin cancer is the most common type of cancer. In 2022 alone, there were over 331,722 cases worldwide, marking a global increase in the disease’s incidence. When it comes to melanoma, timely detection plays a crucial role in effective treatment and patient outcomes.

However, traditional melanoma diagnosis relies on time-consuming, in-person clinical visits and manual image assessment, which may lead to delayed diagnoses and inconsistent accuracy, especially in early-stage detection. To solve this problem, the client required a solution that would automate this process.

Challenge

The projects encompassed the following challenges:

• Streamlining the process of melanoma diagnosis for improved efficiency.
• Ensuring accurate and reliable detection of skin cancer using AI technology.
• Establishing seamless communication between the app and clinics for analysis.
• Developing a user-friendly interface for easy image capture and comparison.

Solution

• iOS and Android application capturing detailed skin images for analysis.
• Database storage enabling easy tracking and analysis of skin dynamics over time.
• Integration of advanced image analysis algorithms for precise diagnosis.
• Seamless transmission of patient's skin images to clinics for analysis.

Result

• Enhanced efficiency in melanoma diagnosis through automated image analysis.
• Improved tracking and analysis of skin dynamics for effective monitoring.
• Streamlined communication between patients and clinics for prompt diagnosis.
• User-friendly interface enabling easy image capture and comparison for patients and healthcare professionals.

Technologies and tools: Android, iOS, Android SDK, iOS SDK, Objective-C