The problem of improving the quality of a convolutional neural network (CNN) in the case of searching for objects on histological scans has been around for a long time and comes down, first of all, to choosing the correct CNN scheme and preparing a dataset of stable quality. The operation of the object detection algorithm is influenced by many factors, including image quality, image size, and the search for the object itself. Search for modern studies demonstrating the influence of various image characteristics on the selection of training programs and the choice of CNN design on the quality of the created model. As research basis, the literature for the past 5years devoted to data preprocessing, methodologies, requirements for images included in datasets, creating images for CNN models, and structure selection issues were analyzed. At the time of the study, the requirements for image sizes were not formulated and there is no data on the sizes of objects to the image sizes for optimizing the model. In addition, the problem of choosing a neural network scheme is not transparent and algorithmic. In most cases, researchers use the structures they developed or used themselves, without explaining the reasons or criteria for their choice, or comparing alternatives. All these issues complicate the process of developing CNN models for digital image processing. Current research presented and provided a brief overview of studies on preparing images for a dataset and possible choice of CNN structure.