Neural Network Technologies in Medical Diagnosis (Review)

Vyucheyskaya, Mariya V.; Kraynova, Irina N.; Грибанов, А. В.

doi:10.17238/issn2542-1298.2018.6.3.284

Cited by 11 publications

(6 citation statements)

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“…Today, there are a large number of deep-learning models used for processing medical texts. The largest number of such models work in the English-speaking field of text analysis [14,15]. However, some models have been pre-trained in Russian.…”

Section: Applying Pre-trained Modelsmentioning

confidence: 99%

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Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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The purpose of this paper is to study approaches to the intellectual processing of Russianlanguage textual medical information concerning the thyroid cytopathology description to solve the issues of their classification and generation of the text of the medical report, as well as augmentation of descriptions in their acute shortage. Over the past decade, the field of biomedicine has not undergone significant changes. Approaches to analyzing patients' problems are mostly based on manual processing and expert knowledge of doctors. The paper considers the creation of a machine-learning pipeline containing a full cycle of data preprocessing and model training in the field of thyroid nodules fine-needle aspiration classification according to the Bethesda thyroid cytopathology reporting system. Sequential and transformer neural networks were used to design the architecture of deep learning models. The paper proposes approaches for cleaning and preprocessing raw medical descriptions to the required type. The obtained results show that sequential neural networks have greater accuracy on small data sets, and transformation architectures are superior to others when generating cytopathological reports on large amounts of data. The solution obtained in the study can be used as an additional reference tool for thyroid cytologists.

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Section: Applying Pre-trained Modelsmentioning

confidence: 99%

“…It consists of the fact that when training a neural network, a word is masked not only at the end of a sentence but also inside it. Such a task is called a masked language modeling task [14].…”

Section: Applying Pre-trained Modelsmentioning

confidence: 99%

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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“…In reality, the symptoms of diseases caused by parasites in the digestive tract are more extensive. [19,20]. Neural network technologies are also used for the diagnostic of diseases of the gastrointestinal tract, for example, for the differential diagnosis of liver diseases [21] and in predicting the development of abdominal sepsis in patients with severe acute pancreatitis [22,23].…”

Section: Immune Disorders and Disease Symptomsmentioning

confidence: 99%

Differential Diagnosis of Parasitic Diseases of the Gastrointestinal Tract Using Artificial Neural Networks

Musevi¹

2021

JCLR

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Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing illnesses as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of its training. For the created neural network, test results for the respective symptoms and disease prediction results for these symptoms were obtained.

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“…Artificial neural networks are effectively used in the diagnosis of various diseases [19,20]. Neural network technologies are also used for the diagnosis of diseases of the gastrointestinal tract, for example, for the differential diagnosis of liver diseases [21] and in predicting the development of abdominal sepsis in patients with severe acute pancreatitis [22,23].…”

Section: A Neural Network Model For Predicting Gastrointestinal Diseamentioning

confidence: 99%

The use of the Neural Network in Predicting a Number of Diseases of the Gastrointestinal Tract caused by Parasites

Musevi¹,

Pashayeva²,

Abdullayev³

2020

GJSFR

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Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing diseases as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of training the network. For the created neural network, test results for the corresponding symptoms and disease prediction results for these symptoms were obtained.

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Neural Network Technologies in Medical Diagnosis (Review)

Cited by 11 publications

References 0 publications

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Differential Diagnosis of Parasitic Diseases of the Gastrointestinal Tract Using Artificial Neural Networks

The use of the Neural Network in Predicting a Number of Diseases of the Gastrointestinal Tract caused by Parasites

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