Non-invasive skin cancer diagnostic methods develop rapidly thanks to Deep Learning and Convolutional Neural Networks (CNN). Currently, two types of diagnostics are popular: (a) using single image taken under white illumination and (b) using multiple images taken in narrow spectral bands. The first method is easier to implement, but it is limited in accuracy. The second method is more sensitive, because it is possible to use illumination considering the absorption bands of the skin chromophores and the optical properties of the skin. Currently CNN use a single white light image, due to the availability of large datasets with lesion images. Since CNN processing and analysis requires a large image database, only mathematical models have been used for multispectral diagnostics so far. Several scientific groups have created unique CNN, but the possibility of sharing pre-trained CNN models is limited due to the diversity of spectral bands used for skin lesion imaging. Current CNN models require image sets where each skin lesion has the same number of specific spectral bands. Therefore, researchers are unable to share their trained CNN models and each team uses a limited amount of skin lesions for CNN training. The paper proposes multi-input CNN architecture with a special encoding layer that allows using images in arbitrary spectral bands. That will allow sharing pre-trained models with other researchers to fine tune the model using additional wavebands. Additionally, the proposed model can adopt images taken under white illumination. As a result, it will be able to increase current melanoma detection accuracy.