The problem of inverse correlation filters design to recognize a set of objects is considered as the problem of regression parameters estimation on the base of input data arrays and desirable response. The data and response should be processes with zero mean to consider this problem as evaluation of regression parameters. The problem is solved using the least squares method with regularization. The regularization is optimized to achieve high resolution of the filters in conjunction with capture’ broad band of objects given by a set of templates. The least squares method is using in the terms of singular value decomposition that made it possible to linearize the nonlinear ridge regression optimization problem. The methods to false recognitions elimination are considered, It was shown that the regression approach gives additional condition to recognize classes of objects. This allows to have more high accuracy in recognition of desired objects on a foreign background in comparison with other correlation filters types.
Modern biomedical engineering is characterized by the rapid growth of data volumes that require processing and analysis to support clinical decision-making. Information technology plays a key role in ensuring the high-performance processing of these large datasets, contributing to the increased accuracy and speed of clinical diagnoses, as well as more effective subsequent patient treatment. This article aims to review current approaches and technologies used for biomedical data processing and to rethink the approach to using big data in decision support systems. Special attention is given to machine learning methods that enhance data analysis efficiency. The data processing approach proposed in this article allows for an 10-12% increase in the accuracy of spinal pathology classification, confirming its feasibility in medical practice.
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