Information from metagenomic data from human microbiome may improve diagnosis and prognosis for multiple human diseases. However, to achieve a prediction based on bacterial abundance information remains a challenge. Indeed, the number of features being much higher than the number of samples, we face difficulties related to high dimensional data processing, as well as overfitting. In this study, we investigate several convolutional neural network architectures for synthetic images and some experimental techniques to generate and train these synthetic images. We also explore supervised learning for visualizing high dimensional data that use data on genus, species and higher taxonomic level information. In addition, some dimensionality reduction approaches are examined on very high dimensional data such as gene families abundance. We evaluated our approach on six different metagenomic datasets including five types of diseases with more than 1000 samples. Our method displays promising results and can be used in different omics data settings, including integrative ones.