Early detection of melanoma using multispectral imaging and artificial intelligence techniques

Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%.