Перегляд за Автор "Barmak, Olexander"
Зараз показуємо 1 - 13 з 13
Результатів на сторінці
Налаштування сортування
Документ An approach to early diagnosis of pneumonia on individual radiographs based on the CNN information technology(Bentham Open, 2021-11-19) Radiuk, Pavlo; Barmak, Olexander; Krak, IuriiAim: This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays. Background: For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment. Objective: The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis. Methods: Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features. Results: An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making. Conclusion: According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.Документ Analysis of deep learning methods in adaptation to the small data problem solving(Springer Cham, 2022-09-14) Krak, Iurii; Kuznetsov, Vladyslav; Kondratiuk, Serhii; Azarova, Larisa; Barmak, Olexander; Radiuk, PavloThis paper discusses a specific problem in the study of deep neural networks – learning on small data. Such issue happens in situation of transfer learning or applying known solutions on new tasks that involves usage of particular small portions of data. Based on previous research, some specific solutions can be applied to various tasks related to machine learning, computer vision, natural language processing, medical data study and many others. These solutions include various methods of general purpose machine and deep learning, being successfully used for these tasks. In order to do so, the paper carefully studies the problems arise in the preparation of data. For benchmark purposes, we also compared “in wild” the methods of machine learning and identified some issues in their practical application, in particular usage of specific hardware. The paper touches some other aspects of machine learning by comparing the similarities and differences of singular value decomposition and deep constrained auto-encoders. In order to test our hypotheses, we carefully studied various deep and machine learning methods on small data. As a result of the study, our paper proposes a set of solutions, which include the selection of appropriate algorithms, data preparation methods, hardware optimized for machine learning, discussion of their practical effectiveness and further improvement of approaches and methods described in the paper. Also, some problems were discussed, which have to be addressed in the following papers.Документ Detection of early pneumonia on individual CT scans with dilated convolutions(CEUR-WS, 2021-04-23) Krak, Iurii; Barmak, Olexander; Radiuk, PavloOver the past decades, pneumonia has been considered one of the most dangerous diseases, leading to severe consequences in a short time. Without proper and timely treatment, pneumonia can lead to fatal consequences. Thus, early diagnosis and detection of this lung disease are crucial in successful treatment and constant monitoring. Indeed, there is a high demand for the development of medical image technologies for disease identification. In this paper, we propose a novel information technology for robust feature identification and early detection of pneumonia on computer tomography scans. We also propose a new modified convolutional neural network as a core feature extractor. An effective dilated convolution operation with different rates, combining features of various receptive fields, was utilized to detect and analyze visual deviations in targeted images. Due to applying the dilated convolutions, the network avoids significant losses of objects' spatial information while providing low computational losses. The investigated model classifies computed tomography images with a validation accuracy of up to 96.12%. Overall, our approach requires much fewer computing resources, proving its effectiveness for solving practical problems on available computing devices.Документ ECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System(CEUR-WS.org, 2024-07-29) Kovalchuk, Oleksii; Barmak, Olexander; Radiuk, Pavlo; Krak, IuriiIn modern healthcare, timely and precise diagnosis of arrhythmias can significantly impact patient outcomes, as arrhythmias are indicative of various cardiac disorders that require immediate attention. The classification of these irregular heartbeats based on electrocardiogram (ECG) signals is essential for the development of intelligent healthcare systems that can provide real-time monitoring and diagnosis, integrating seamlessly into smart city infrastructures and IoT-enabled smart homes. In this paper, we propose novel methods to enhance the classification and interpretation of arrhythmia by ECG signals based on convolutional neural network (CNN). Leveraging the MIT-BIH Arrhythmia Database, which includes 48 recordings from 47 patients, the proposed approach involved preprocessing the ECG signals into fragments and enhancing the CNN architecture with Batch Normalization layers and an additional convolutional layer. The network was trained and validated using statistical metrics namely accuracy, precision, recall, and F1-scores. The results demonstrated an overall classification accuracy of 99.43%, with particularly high precision and recall for Normal beats, Right bundle branch block beats, and Left bundle branch block beats, achieving F1-scores close to 100%. The introduced CNN showed superior performance in distinguishing between nine types of arrhythmias. However, the study highlighted the limitation of relying on clinical features for decision justification, especially in cases of overlapping pathologies. Overall, the findings suggest that the proposed approach can serve as a reliable supporting tool for arrhythmia diagnosis, offering high accuracy and potential integration into real-time monitoring systems.Документ Method of facial geometric feature representation for information security systems(CEUR-WS, 2022-06-17) Kalyta, Oleg; Iurii, Krak; Barmak, Olexander; Wojcik, Waldemar; Radiuk, PavloThroughout human history, emotional manifestations have played a major role in interpersonal interaction among humans in all areas of society. In particular, information security systems for visual surveillance, based on recognizing emotional states by facial expressions, have recently become highly relevant. In this paper, we propose a method of representing geometric facial features, which aims to enhance the functioning of visual surveillance for information security systems. The method is designed to automatically reflect the facial expressions of human emotions in the form of quantitative characteristics of geometric shapes. It uses software-generated landmarks for constructing specific geometric characteristics of the face, which serve as input data for the method. Our method consists in forming seven geometric shapes based on predefined landmarks, with the subsequent quantitative expression of these shapes. The method derives quantitative features of seven forms, which are further used to identify emotional facial states. We validated the proposed method using hyperplane classification and compared its performance with analogs. As such, the classification model, which was constructed based on the proposed method, achieved a classification accuracy of 92.73% and slightly surpassed the analogs in other statistical indicators. Overall, the results of computational experiments confirmed the effectiveness of the proposed method for identifying changes in a person’s emotional state by facial expressions. In addition, the use of simple mathematical calculations in our method has significantly reduced the computational complexity against analogs.Документ Modeling the assessment of credit risk losses in banking(Special Edition of International Conference on Monitoring, Modeling &Management of Emergent Economy (M3E2-MLPEED 2020) at CEUR WorkshopProceedings, 2020) Larionova, Каterina; Donchenko, Tetyana; Oliinyk, Andriy; Kapinos, Hennadii; Savenko, Oleg; Barmak, OlexanderThe article develops a model of credit risk assessment within the scope of the variability concept that can be used for verification of new methods for borrowers’ credit capacity estimation, the acceptable level of credit risk forecasting and its early prediction. It is aimed to be used during the automated banking systems development. The proposed model of credit risk assessment has been tested on the basis of the data from one of the Ukrainian banks. To determine the adequacy of this model has been proved by the comparison analysis of the proposed model with the results obtained by the National Bank of Ukraine methodology.Документ Myocardium Segmentation using Two-Step Deep Learning with Smoothed Masks by Gaussian Blur(CEUR.org, 2023-01-07) Slobodzian, Vitalii; Radiuk, Pavlo; Zingailo, Anastasiia; Barmak, Olexander; Krak, IuriiNowadays, cardiac magnetic resonance images face challenges in distinguishing between inflamed and non-inflamed tissues due to subtle color variations rather than clear density distinctions. Pixel values in these images vary based on individual subjects and the MRI equipment, making them inconsistent across different training datasets. Thus, detecting inflamed tissues in MRIs largely depends on the expertise of interpreting physicians, making it time-consuming and complicating the training of accurate classifiers. To address this issue, in this study, we propose a novel approach for myocardium segmentation on MRI images utilizing a two-stage neural network process coupled with mask refinement. The initial network outlines the myocardium, which is then fine-tuned by the second network for precise myocardium segmentation. A key enhancement involves mask post-processing via Gaussian blur, where the blur coefficient is automatically adjusted. Experimental outcomes demonstrated an increase in the Dice coefficient from 0.889 to 0.894 upon removing non-essential labels. Moreover, using a dual-model approach for myocardium localization and contour definition elevated the coefficient to 0.938. Employing the Gaussian blur during mask resizing culminated in an impressive average Dice coefficient of 0.955.Документ Robust R-peak Detection using Deep Learning based on Integrating Domain Knowledge(CEUR.org, 2023-01-07) Kovalchuk, Oleksii; Radiuk, Pavlo; Barmak, Olexander; Krak, IuriiElectrocardiography (ECG) is a pivotal clinical technique for assessing heart function by recording its electrical activity. However, accurate processing and analysis of ECG signals, particularly the detection of R-peaks, remains challenging. Any inaccuracies in R-peak detection can significantly impact subsequent stages of analysis, potentially leading to incorrect diagnoses and treatment decisions. Therefore, in this study, we aim to refine the approach to identifying R-peaks in ECG signals by integrating knowledge of a reference ECG signal into the input signal, addressing the critical need for accurate R-peak detection in diagnosing various cardiac pathologies. The authors propose a novel method involving the integration of knowledge into the ECG signal, processing this information using a convolutional neural network, and post-processing the CNN model's results to identify R-peaks. The method was evaluated using various four well-known ECG databases. Comparative results, with an error margin of +-25 ms, revealed that the proposed approach was the top performer across almost all metrics and databases, frequently achieving accuracy scores of 0.9999 and demonstrating high precision, recall, and F1-score. Based on the investigation findings, the proposed approach is robust and reliable, with the best performance achieved on the QT database test set, offering a balanced and dependable solution for R-peak detection in ECG signals.Документ Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures(Publishing house "Academperiodika", 2023-01-23) Manziuk, Eduard; Barmak, Olexander; Krak, Iurii; Pasichnyk, Olexander; Radiuk, Pavlo; Mazurets, OlexanderThe presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontology and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.Документ Web-based information technology for classifying and interpreting early pneumonia based on fine-tuned convolutional neural network(Khmelnytskyi National University, 2021-05-31) Radiuk, Pavlo; Barmak, Olexander; Радюк, Павло; Бармак, ОлександрThere have been rapid development and application of computer methods and information systems in digital medical diagnosis in recent years. However, although computer methods of medical imaging have proven helpful in diagnosing lung disease, for detecting early pneumonia on chest X-rays, the problem of cooperation between professional radiologists and specialists in computer science remains urgent. Thus, to address this issue, we propose information technology that medical professionals can employ to detect pneumonia on chest X-rays and interpret the results of the digital diagnosis. The technology is presented as a web-oriented system with an available and intuitive user interface. The information system contains three primary components: a module for disease prediction based on a classification model, a module responsible for hyperparameter tuning of the model, and a module for interpreting the diagnosis results. In combination, these three modules form a feasible tool to facilitate medical research in radiology. Moreover, a web-based system with a local server allows storing personal patient data on the user's computing device, as all calculations are performed locally.Документ Квантові згорткові нейронні мережі: особливості реалізації у технічних, природничих і соціально-економічних системах(Хмельницький національний університет, 2023) Гринько, Ірина; Скрипник, Тетяна; Бармак, Олександр; Hrynko, Iryna; Skrypnyk, Tetyana; Barmak, OlexanderУ роботі проведені аналіз та дослідження застосування квантових згорткових нейронних мереж для технічних, природничих і соціально-економічних інформаційних систем. Квантові згорткові нейронні мережі є новим підходом до обробки інформації, який базується на принципах квантової механіки та штучного інтелекту. В технічних системах досліджено можливість застосування квантових згорткових нейронних мереж для розв'язання складних задач, таких як спроби злому криптографічних ключів та криптографічного шифрування. Результати показали, що квантові згорткові нейронні мережі можуть забезпечити більш точні та швидкі обчислення в порівнянні з класичними нейронними мережами. У природничих системах проведено дослідження використання квантових згорткових нейронних мереж для моделювання та прогнозування складних природних процесів. Досліджено їхню ефективність у розумінні та вивченні складних молекулярних структур. Виявлено, що квантові згорткові нейронні мережі можуть забезпечити більш точні та швидкі результати у порівнянні зі звичайними методами обробки даних. У соціально-економічних системах досліджено можливості використання квантових згорткових нейронних мереж для аналізу соціальних мереж, прогнозування фінансових ринків та криптовалют. Виявлено, що застосування квантових згорткових нейронних мереж може покращити точність прогнозування та забезпечити більш ефективне прийняття рішень у соціально-економічних системах. Результати дослідження підтвердили, що квантові згорткові нейронні мережі мають потенціал для використання в різних сферах, включаючи технічні, природничі та соціально-економічні системи. Вони здатні досягти більшої точності, швидкості обробки та прогностичної здатності порівняно з традиційними методами.Документ Метод виявлення багатороторних бпла засобами штучного інтелекту(Хмельницький національний університет, 2023) Горєлов, Сергій; Бармак, Олександр; Манзюк, Едуард; Gorelov, Serhii; Barmak, Olexander; Manziuk, EduardЗростаюче використання безпілотних літальних апаратів (БПЛА) у різноманітних комерційних та технічних секторах підкреслило необхідність надійного спостереження за БПЛА, особливо з огляду на питання громадської безпеки. У відповідь на цю вимогу було швидко розвинуто методи ідентифікації БПЛА. Незважаючи на прогрес, маленький розмір дронів, складні умови повітряного простору та зміни у освітленні залишаються значними викликами в цій сфері досліджень. З метою розв'язання цих викликів проведено дослідження, що базується на новому підході до виявлення БПЛА невеликих розмірів, використовуючи вдосконалену версію YOLOv8. Запропоновано метод, який дозволяє інтегрувати модуль високої деталізації для підвищення точності виявлення дрібних повітряних об'єктів. Також було оптимізовано архітектуру шляхом видалення елементів, що є більш важливими для великих об'єктів, тим самим зменшуючи обчислювальне навантаження та прискорюючи швидкість виявлення БПЛА. Крім того, використання Swin Transformer дозволяє краще виявляти об'єкти різних масштабів та зменшує обчислювальну складність. Також впровадження модуля уваги GAM у архітектуру мережі значно підвищує здатність до асиміляції ознак, що призводить до помітного підвищення ефективності виявлення БПЛА. У порівнянні з оригінальною базовою моделлю, наш запропонований метод демонструє суттєві покращення, збільшуючи P (precision), R (recall) та mAP (mean average precision) на 9%, 10,2% та 6% відповідно. Крім того, він істотно оптимізує модель за кількістю вхідних параметрів на 32,5% та 32,4% відповідно. Запропонований метод у порівняльних експериментах та експериментах зі специфічними наборами даних показав велику перспективу для практичної реалізації в системах виявлення об'єктів БПЛА, що дає підстави говорити про перспективу застосування в реальних умовах.Документ Інформаційна технологія візуального аналізу рентгенівських зображень для інтерпретації результатів діагностування пневмонії(Хмельницький національний університет, 2021) Бармак, О.В.; Радюк, П.М.; Barmak, Olexander; Radiuk, PavloНа сьогодні пневмонія є одним із поширеніших та найбільш серйозних легеневих захворювань у всьому світі. Раннє діагностування пневмонії є ключовим чинником її успішного лікування. Для розв’язання актуального завдання в галузі цифрового діагностування в цій пропонується інформаційна технологія візуального аналізу рентгенівських зображень для пояснення результатів діагностування пневмонії. В основі технології закладено модель класифікації на основі згорткової нейронної мережі для вилучення слабо виражених ознак ранньої вірусної пневмонії та модифікований метод відмінної локалізації для інтерпретації результатів класифікації. Метод інтерпретації полягає в застосуванні зважених градієнтів до мап активації класів. Подібна модифікація забезпечує відмінну локалізацію аномальних зон на рентгенограмах, що дає змогу вилучити цільові слабко виражені ознаки ранньої пневмонії. Відповідно до обчислювальних експериментів, запропонована інформаційна технологія може бути ефективним засобом для миттєвого діагностування в разі перших підозр на виявлення пневмонії.