• Ashwini R
• Swetha K
• Santhosh Aboorva J
• Viknesh A R
• Marshal M G

The development of a skin cancer detection system using a Convolutional Neural Network (CNN) implemented on Raspberry Pi hardware. The CNN model is designed for efficient real-time processing of dermatoscopic images, providing a portable and accessible solution for skin cancer diagnosis. Leveraging the computational capabilities of the Raspberry Pi, the system incorporates a high-resolution camera module to capture skin lesion images for immediate analysis. By optimizing the CNN architecture for edge computing, the implementation strikes a balance between accuracy and computational efficiency. This hardware-based approach facilitates on-the-spot skin cancer detection, promoting point-of-care applications and demonstrating the practicality of deploying advanced deep learning models on resource-constrained devices for critical healthcare tasks.

Skin Cancer, CNN Architecture, Layers, Improve Accuracy, Machine learning

"SKIN CANCER DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h157-h165, April-2024, Available :http://www.jetir.org/papers/JETIR2404719.pdf

"SKIN CANCER DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph157-h165, April-2024, Available at : http://www.jetir.org/papers/JETIR2404719.pdf