Identification Of Melanoma from Hyperspectral Pathological Image

Melanoma is responsible for 75% of skin cancer-related deaths, but early detection and treatment offer a 99% survival rate. Traditional diagnostic methods include dermoscopic and histopathological analysis. However, existing systems rely on 2D dermoscopic images, which lose crucial features due to their limited dimensionality, reducing diagnostic accuracy. Additionally, 2D images fail to provide early-stage melanoma detection. To overcome these limitations, we propose using 3D hyperspectral pathology images, which preserve all essential features during extraction, ensuring more accurate results. Our system employs a deep convolutional network for melanoma segmentation with enhanced precision. A three-dimensional convolutional network further analyzes hyperspectral images, leveraging spectral features to improve melanoma detection. By utilizing 3D hyperspectral imaging and deep learning techniques, our approach enhances early detection and diagnostic accuracy, ultimately increasing survival rates. This advanced method significantly improves upon conventional 2D imaging systems in melanoma diagnosis and treatment planning.