Supervisor's review for the bachelor's thesis of the 4th year student of the Informatics Department of the Faculty of Mathematics and Mechanics Efremov A.A.  Detecting Anomalies in Chest X-Ray Images by means of Deep Learning with image pre-processing by Lung Segmentation and Bone Suppression Techniques  Bachelor's work of Efremov A.A. 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 aim of the study was to investigate the effect of segmentation and bone removal techniques on the classification of normal images and images with pathologies on existing open chest radiographs.  The following results are achieved in the work: • Trained a neural network with the architecture of U-Net to identify areas of interest; • Implemented the algorithm for filtering the image of bones; • The neural network with AlexNet architecture is trained; • The quality of classification is compared, the best quality is provided by the algorithm with the image filtration of bones but without segmentation;  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: • F1 measure is an inadequate task of finding anomalies. For practical use, an unbalanced measure is required that must be taken into account in training, which was not done; • The classifier uses an outdated network architecture with shallow depth; • the text of the thesis note has some stylistic shortcomings.  Despite the shortcomings, considering the valuable practical results of the work, I believe that the bachelor's thesis of Efremov A.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