Open AccessGraphical data processing in the Clinical Decision Making System for the Respiratory Diseases Diagnosis using ML methods
Author(s) -
Gyuzel Shakhmametova,
Nafisa Yusupova,
Alexander Evgrafov,
Rustem Zulkarneev
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1069/1/012009
Subject(s) - computer science , preprocessor , convolutional neural network , dicom , artificial intelligence , image processing , python (programming language) , jpeg , visualization , data pre processing , data mining , pattern recognition (psychology) , programming language , data compression , image (mathematics)
This article considers the basic algorithm of graphic data extraction used in the developed system of clinical decision-making in diagnosis of respiratory diseases, methods for processing files in JPEG and DICOM formats, visualizing and preprocessing images as well as constructing neural network models based on a convolutional neural network that provide detection symptoms of pneumonia in patients based on radiographs. Data extraction and processing are performed in the “Python” programming language using additional libraries: “pydicom” for processing DICOM files. “Pillow” for visualization, “Keras” for building a convolutional neural network model. The relevance of the task is due to the large volumes of graphical data supplied to the input of the CDSS and necessary for its effective functioning. The novelty of this development lies in the application of a set of existing and development of new algorithms for extracting and processing graphic information.