Skin cancer detection ecosystem

Cancer is a complex and daunting disease, but early detection is crucial for successful treatment. Melanoma is an aggressive form of skin cancer that requires prompt diagnosis and treatment.

To enhance doctors' skin cancer detection capabilities, I worked on a project that aimed to develop an ecosystem consisting of an easy-to-manufacture data collection device and software for decision making based on algorithms trained on datasets collected in the field.

One of the biggest challenges we faced was the complexity of skin and its electrical properties, which are crucial for detection. Additionally, the limited number of studies on this topic made the task more challenging.

To manage the project efficiently, we divided it into five parts: overall design and requirements, sub-system development and testing, full-system integration and testing, system validation in a real environment, and pilot user experience testing. By following these guidelines, we identified potential issues early on, allowing us to prioritize tasks, highlight dependencies, and avoid costly changes during development.

Despite limited resources and other challenges, we successfully delivered two generations of prototypes. These prototypes were valuable tools for medical research at the Aristotle University of Thessaloniki (AUTH) medical school and Papageorgiou Hospital.

However, due to the lack of sufficient resources to hire a development team, we were unable to continue the project. Nonetheless, the insights we gained from this project have provided valuable information for the development of future initiatives aimed at improving skin cancer detection and treatment.