Supervisor's review for the bachelor's thesis of the 4th year student of the Department of Informatics of the Mathematics and Mechanics Department Tkacheva D.A.  Аnоmаly dеtесtiоn in prеvеntive fluоrоgrаphiс   Bachelor's work of Tkacheva is devoted to the problem of detecting anomalies on chest x-ray images. The task is actual due to the shortage of qualified radiologists for the primary description of the pictures in a massive regular population survey, which is the main method of early detection of tuberculosis, other chronic infectious diseases and oncological diseases of the chest.  The purpose of the study was to investigate the use of a convolutional autoencoder to reduce the dimensionality of the feature space when learning an unchanged X-ray database provided by the St. Petersburg Institute of Pulmonology  The following results are achieved in the work: • The convolutional auto-encoder is trained; • Implemented hierarchical classifier from the convolutional auto-encoder and SVM; • The quality of the classification has been studied, with a fullness of 0.99, the accuracy is more than 0.75;  Provided that metrics are not subject to systematic error, the quality of anomaly detection is sufficient for the practical application of the method. The results are of great practical importance for the choice of the network architecture in solving the problem of finding anomalies.  The work has also disadvantages: • judging by the experimental data, in the training sample the classes are essentially unbalanced, the results require additional verification on another set of data; • the text of the thesis note has some stylistic shortcomings.  Despite the shortcomings, given the valuable practical results of the work, I believe that the bachelor's thesis of Tkacheva D.A. deserves an evaluation - "EXCELLENT".  PhD., senior lecturer of Department of Informatics Faculty of Mathematics and Mechanics of St. Petersburg State University Salischev S.I. 05/15/2018