The thesis describes the development of an interactive image visualization tool. To improve the display of images, the tool combines the use of deep learning with other data analytics techniques. The proposed solution consists of several sequentially connected methods. The first step is using convolutional neural networks for image processing to acquire features which can be used for image comparison. Then, optimization algorithms are used to place images into a two-dimensional rectangular similarity grid where similar images are placed closer together. The result of the thesis is an interactive image viewer, designed as a similarity grid. It is implemented in the Python programming language and included in the Orange toolbox as a widget named Image Grid, where it is seamlessly integrated with other widgets in the Image Analytics add-on.