Intellectual Diagnostics of Thyroid Tumors
DOI:
https://doi.org/10.18372/1990-5548.86.20559Keywords:
artificial intelligence, machine learning, neural networks, medical intelligent systems, thyroid cancer, tumor diagnosisAbstract
The article is devoted to the intelligent diagnosis of thyroid tumors, the diagnosis of papillary thyroid cancer based on general information, ultrasound images, and pathohistological images. It examines modern approaches to the intelligent diagnosis of thyroid tumors using machine learning and artificial intelligence methods. The types of medical intelligent systems, their architecture, accuracy, and the set of tasks they perform for the classification of thyroid cancer are considered. The problems of papillary thyroid cancer are considered, the specifics of the disease and the signs by which it is diagnosed are described. The main tasks of an intelligent system capable of automatically analyzing patient medical data and supporting clinical decision-making by an endocrinologist, segmenting and classifying thyroid tumors are outlined. The equipment used to form the training sample is described, and the process of data collection for building an intelligent medical system is described. The task to be solved is set. The metrics by which the accuracy of the intelligent medical system will be evaluated are characterized. The architecture of the intelligent medical system is presented, its main blocks are characterized, and the functionality of each block is described. A UML diagram is presented, according to which the intelligent medical system will operate. The data that will be used to form the training sample is presented, indicating its type, dimension, how the data is collected, and how this data will be used to train the intelligent medical system. The results of the study are aimed at improving the effectiveness of early detection of thyroid pathologies and reducing the number of false diagnoses, creating a convenient tool that will reduce the time it takes for a doctor to diagnose the disease and increase the accuracy of diagnosing papillary thyroid cancer.
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