Human-computer interaction tool development for skin cancer diagnosis on a low-cost platform
Introduction: This paper is the result of the research project “Deep Learning for the Identification of Non-Melanocytic Lesions on Dermoscopic Images,” conducted at Francisco de Paula Santander University in 2024. A computer-aided diagnostic tool is proposed for skin cancer classification, aimed at facilitating the automatic analysis of skin lesions on low-cost platforms.
Methods: A graphical user interface was developed in Python, integrating a convolutional neural network model implemented on a Raspberry Pi. The system’s performance was evaluated through real-world testing.
Results: The average diagnosis time was 13.6 seconds. Mean CPU usage was 32.65%, RAM usage reached 46.70%, and processor temperature peaked at 55 °C under maximum load.
Conclusions: The system operates effectively on embedded devices, with good performance and moderate resource consumption, making it suitable for resource-constrained environments.
Originality: The authors declare that the work is original and unpublished.
Limitations: The study focuses on the development of the human-computer interaction tool, not on the development of the computational model.
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