Research and development of algorithms for the formation of an effective ensemble of convolutional neural networks for image classification

Abstract

The object of the research is artificial neural networks (ANN) with convolutional architecture for image classification. The subject of the research is the study and development of algorithms for constructing ensembles of convolutional neural networks (SNS) in conditions of limited training sample. The aim of the study is to develop an algorithm for the formation of an effective model based on an ensemble of convolutional SNS using methods of averaging the results of each model, capable of avoiding overfitting in the process of improving the accuracy of the forecast and trained on a small amount of data, less than 10 thousand examples. As a basic network, an effective SNA architecture was developed as part of the ensemble, which showed good results as a single model. The article also examines methods for combining the results of ensemble models and provides recommendations for the formation of the SNA architecture. The research methods used are the theory of neural networks, the theory of machine learning, artificial intelligence, methods of algorithmization and programming of machine learning models, a comparative analysis of models based on different algorithms using classical ensembling with simple averaging and combining the results of basic algorithms in conditions of limited sampling, taking into account weighted average. The field of application of the obtained algorithm and model is medical diagnostics in medical institutions, sanatoriums during primary diagnostic admission, using the example of a research task, the model is trained to classify dermatological diseases according to input photographs. The novelty of the study lies in the development of an effective algorithm and image classification model based on an ensemble of convolutional NS that exceed the prediction accuracy of basic classifiers, the process of retraining an ensemble of classifiers with deep architecture on a small sample volume is investigated, from which conclusions are drawn on the design of an optimal network architecture and the choice of methods for combining the results of several basic classifiers. As a result of the research, an algorithm has been developed for the formation of an ensemble of SNS based on an effective basic architecture and weighted average averaging of the results of each model for the classification task of image recognition in conditions of limited sampling.