Multi-Class Classification of Melanoma on an Edge Device

Melanoma is a dangerous skin cancer that requires early detection for successful treatment. This study presents an implantable diagnostic device that points to revolutionize early detection of melanoma. The device combines the most recent designs with re-configurable computing strategies, permitting for precise diagnosis and analysis of potential melanoma pictures. Furthermore, it utilizes advanced hardware and software components. Utilizing the VGG-16 profound learning model and augmented HAM10000 dataset, this study accomplishes a staggering accuracy rate. It is critical that a Raspberry Pi was utilized to test the usefulness of this model. Thorough tests and experiments have affirmed that this device can enormously improve the speed and accuracy of melanoma detection, potentially making a life-saving contribution within the field of early detection of melanoma. This study highlights the significance of utilizing state-of-the-art innovation in the battle against skin cancer and offers a promising arrangement for real-time detection of melanoma.