Júlia Calcagno Mayer (2008), Isabelly Heloisa Marques (2008)
Colégio Sinodal da Paz, Novo Hamburgo, Brasil
The Functionality and Implementation of Deep Convolutional Neural Networks (Artificial Intelligence) in the Detection and Diagnosis of Superficial Extensive Melanomas – Phase II
After constant advances, transformations, and changes in technology during the last decades, a new mechanism has emerged in the field of computer science, aimed at developing systems capable of simulating human skills. This mechanism presents multiple layers of neurons, similar to those in the human brain, and has been widely used with great success in image recognition, speech processing, and object detection. Known as Deep Convolutional Neural Networks, these systems have proven to be highly effective in various applications. Therefore, this project explores their functionality and execution in detecting and diagnosing extensive superficial melanomas.
Although this device offers significant benefits, it remains relatively unknown in society. The study aims to understand and explain how this form of artificial intelligence can be applied to diagnosing superficial extensive melanomas, a disease with a high incidence rate. By reducing false positives and false negatives, the methodology focuses on developing algorithms and codes for this tool, utilizing Microsoft Copilot Artificial Intelligence. The objective is to produce and implement these codes on a specialized website for automating image analysis, facilitating the detection of melanomas with greater accuracy.
This research supports the understanding that the tool is both powerful and promising in dermatology, contributing significantly to the early identification of extensive superficial melanomas and enhancing the work of dermatological professionals.
