Sanjar Mirzakhalilov

@tuit.uz

Robotics and intelligent systems
Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Sanjar Mirzakhalilov


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https://researchid.co/sanjarmirzaxalilov

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Biomedical Engineering, Computer Networks and Communications
16

Scopus Publications

305

Scholar Citations

11

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Lightweight early detection of knee osteoarthritis in athletes
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Otabek Ismailov, Djamshid Sultanov, et al.
    Scientific Reports, 2025
  • A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population
    Akmalbek Abdusalomov, Sabina Umirzakova, Sanjar Mirzakhalilov, Alpamis Kutlimuratov, Rashid Nasimov, et al.
    Bioengineering, 2025
    Older adults often face significant challenges in understanding medical information due to cognitive aging and limited health literacy. Existing simplification models, while effective in general domains, cannot adapt content for elderly users, frequently overlooking narrative tone, readability constraints, and semantic fidelity. In this work, we propose GENSIM—a Generative Expert-Narrated Simplification Model tailored for age-adapted medical text simplification. GENSIM introduces a modular architecture that integrates a Dual-Stream Encoder, which fuses biomedical semantics with elder-friendly linguistic patterns; a Persona-Tuned Narrative Decoder, which controls tone, clarity, and empathy; and a Reinforcement Learning with Human Feedback (RLHF) framework guided by dual discriminators for factual alignment and age-specific readability. Trained on a triad of corpora—SimpleDC, PLABA, and a custom NIH-SeniorHealth corpus—GENSIM achieves state-of-the-art performance on SARI, FKGL, BERTScore, and BLEU across multiple test sets. Ablation studies confirm the individual and synergistic value of each component, while structured human evaluations demonstrate that GENSIM produces outputs rated significantly higher in faithfulness, simplicity, and demographic suitability. This work represents the first unified framework for elderly-centered medical text simplification and marks a paradigm shift toward inclusive, user-aligned generation for health communication.
  • PixMed-Enhancer: An Efficient Approach for Medical Image Augmentation
    M. J. Aashik Rasool, Akmalbek Abdusalomov, Alpamis Kutlimuratov, M. J. Akeel Ahamed, Sanjar Mirzakhalilov, et al.
    Bioengineering, 2025
    AI-powered medical imaging faces persistent challenges, such as limited datasets, class imbalances, and high computational costs. To overcome these barriers, we introduce PixMed-Enhancer, a novel conditional GAN that integrates the ghost module into its encoder—a pioneering approach that achieves efficient feature extraction while significantly reducing the computational complexity without compromising the performance. Our method features a hybrid loss function, uniquely combining binary cross-entropy (BCE) and a Structural Similarity Index Measure (SSIM), to ensure pixel-level precision while enhancing the perceptual realism. Additionally, the use of conditional input masks offers unparalleled control over the generation of tumor features, marking a breakthrough in fine-grained dataset augmentation for segmentation and diagnostic tasks. Rigorous testing on diverse datasets establishes PixMed-Enhancer as a state-of-the-art solution, excelling in its realism, structural fidelity, and computational efficiency. PixMed-Enhancer establishes a robust foundation for real-world clinical applications in AI-driven medical imaging.
  • Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Ilyos Kalandarov, Dilmurod Mirzaaxmedov, et al.
    Diagnostics, 2025
    Background/Objectives: The early and accurate detection of Coronary Artery Disease (CAD) is crucial for preventing life-threatening complications, particularly among athletes engaged in high-intensity endurance sports. This demographic faces unique cardiovascular risks, as prolonged and intense physical exertion can exacerbate underlying CAD conditions. Studies indicate that while athletes typically exhibit enhanced cardiovascular health, this demographic is not immune to Coronary Artery Disease (CAD) risks. Research has shown that approximately 1–2% of competitive athletes suffer from CAD-related complications, with sudden cardiac arrest being the leading cause of mortality in athletes over 35 years old. High-intensity endurance sports can exacerbate underlying CAD conditions due to the prolonged physical stress placed on the cardiovascular system, making early detection crucial. This study aimed to develop and evaluate a lightweight deep learning model for CAD detection tailored to the unique challenges of diagnosing athletes. Methods: This study introduces a lightweight deep learning model specifically designed for CAD detection in athletes. By integrating ResNet-inspired residual connections into the VGG16 architecture, the model achieves a balance of high diagnostic accuracy and computational efficiency. By incorporating ResNet-inspired residual connections into the VGG16 architecture, the model enhances gradient flow, mitigates vanishing gradient issues, and improves feature extraction of subtle morphological variations in coronary lesions. Its lightweight design, with only 1.2 million parameters and 3.5 GFLOPs, ensures suitability for real-time deployment in resource-constrained clinical environments, such as sports clinics and mobile diagnostic systems, where rapid and efficient diagnostics are essential for high-risk populations. Results: The proposed model achieved superior performance compared to state-of-the-art architectures, with an accuracy of 90.3%, recall of 89%, precision of 90%, and an AUC-ROC of 0.912. These metrics highlight its robustness in detecting and classifying CAD in athletes. The model lightweight architecture, with only 1.2 million parameters and 3.5 GFLOPs, ensures computational efficiency and suitability for real-time clinical applications, particularly in resource-constrained settings. Conclusions: This study demonstrates the potential of a lightweight, deep learning-based diagnostic tool for CAD detection in athletes, achieving a balance of high diagnostic accuracy and computational efficiency. Future work should focus on integrating broader dataset validations and enhancing model explainability to improve adoption in real-world clinical scenarios.
  • Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Otabek Ismailov, Djamshid Sultanov, et al.
    Diagnostics, 2025
    Background/Objectives: Sports-related bone fractures are a common challenge in sports medicine, requiring accurate and timely diagnosis to prevent long-term complications and enable effective treatment. Conventional diagnostic methods often rely on manual interpretation, which is prone to errors and inefficiencies, particularly for subtle and localized fractures. This study aims to develop a lightweight and efficient deep learning-based framework to improve the accuracy and computational efficiency of fracture detection, tailored to the needs of sports medicine. Methods: We proposed a novel fracture detection framework based on the DenseNet121 architecture, incorporating modifications to the initial convolutional block and final layers for optimized feature extraction. Additionally, a Canny edge detector was integrated to enhance the model ability to detect localized structural discontinuities. A custom-curated dataset of radiographic images focused on common sports-related fractures was used, with preprocessing techniques such as contrast enhancement, normalization, and data augmentation applied to ensure robust model performance. The model was evaluated against state-of-the-art methods using metrics such as accuracy, recall, precision, and computational complexity. Results: The proposed model achieved a state-of-the-art accuracy of 90.3%, surpassing benchmarks like ResNet-50, VGG-16, and EfficientNet-B0. It demonstrated superior sensitivity (recall: 0.89) and specificity (precision: 0.875) while maintaining the lowest computational complexity (FLOPs: 0.54 G, Params: 14.78 M). These results highlight its suitability for real-time clinical deployment. Conclusions: The proposed lightweight framework offers a scalable, accurate, and efficient solution for fracture detection, addressing critical challenges in sports medicine. By enabling rapid and reliable diagnostics, it has the potential to improve clinical workflows and outcomes for athletes. Future work will focus on expanding the model applications to other imaging modalities and fracture types.
  • Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Abror Shavkatovich Buriboev, Azizjon Meliboev, et al.
    Bioengineering, 2025
    The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions. The modified RetinaNet achieves an average precision (AP) of 32.1, surpassing state-of-the-art models in small tumor detection (APS: 14.3) and large tumor localization (APL: 49.7). Furthermore, the model significantly reduces computational costs, making real-time analysis feasible on low-power hardware. Clinical relevance is a key focus of this work. The proposed model addresses the diagnostic challenges of small, variable-sized tumors often overlooked by existing methods. Its lightweight architecture enables accurate and timely tumor localization on portable devices, bridging the gap in diagnostic accessibility for underserved regions. Extensive experiments on the BRATS dataset demonstrate the model robustness across tumor sizes and configurations, with confidence scores consistently exceeding 81%. This advancement holds the potential for improving early tumor detection, particularly in remote areas lacking advanced medical infrastructure, thereby contributing to better patient outcomes and broader accessibility to AI-driven diagnostic tools.
  • Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation
    Kuldashboy Avazov, Sanjar Mirzakhalilov, Sabina Umirzakova, Akmalbek Abdusalomov, Young Im Cho
    Bioengineering, 2024
    Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes. To address these challenges, this paper proposes a novel modification to the U-Net architecture by integrating a spatial attention mechanism designed to dynamically focus on relevant regions within MRI scans. This innovation enhances the model’s ability to delineate fine tumor boundaries and improves segmentation precision. Our model was evaluated on the Figshare dataset, which includes annotated MRI images of meningioma, glioma, and pituitary tumors. The proposed model achieved a Dice similarity coefficient (DSC) of 0.93, a recall of 0.95, and an AUC of 0.94, outperforming existing approaches such as V-Net, DeepLab V3+, and nnU-Net. These results demonstrate the effectiveness of our model in addressing key challenges like low-contrast boundaries, small tumor regions, and overlapping tumors. Furthermore, the lightweight design of the model ensures its suitability for real-time clinical applications, making it a robust tool for automated tumor segmentation. This study underscores the potential of spatial attention mechanisms to significantly enhance medical imaging models and paves the way for more effective diagnostic tools.
  • Lightweight Super-Resolution Techniques in Medical Imaging: Bridging Quality and Computational Efficiency
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Zaripova Dilnoza, Kudratjon Zohirov, Rashid Nasimov, et al.
    Bioengineering, 2024
    Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications.
  • GAN-Based Novel Approach for Generating Synthetic Medical Tabular Data
    Rashid Nasimov, Nigorakhon Nasimova, Sanjar Mirzakhalilov, Gul Tokdemir, Mohammad Rizwan, et al.
    Bioengineering, 2024
    The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving “Good” similarity and “Excellent” utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.
  • A Multi-Scale Approach to Early Fire Detection in Smart Homes
    Akmalbek Abdusalomov, Sabina Umirzakova, Furkat Safarov, Sanjar Mirzakhalilov, Nodir Egamberdiev, et al.
    Electronics Switzerland, 2024
    In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.
  • Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
    Alpamis Kutlimuratov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Oteniyazov, Sanjar Mirzakhalilov, Taeg Keun Whangbo
    Sensors, 2022
  • Comparative Analysis of the Results of EMG Signal Classification Based on Machine Learning Algorithms
    Adilbek Turgunov, Kudratjon Zohirov, Rashid Nasimov, Sanjar Mirzakhalilov
    International Conference on Information Science and Communications Technologies Applications Trends and Opportunities Icisct 2021, 2021
  • A new approach to classifying myocardial infarction and cardiomyopathy using deep learning
    Rashid Nasimov, Bakhodir Muminov, Sanjar Mirzahalilov, Nigorakhon Nasimova
    2020 International Conference on Information Science and Communications Technologies Icisct 2020, 2020
  • Algorithm of Automatic Differentiation of Myocardial Infarction from Cardiomyopathy based on Electrocardiogram
    Rashid Nasimov, Bakhodir Muminov, Sanjar Mirzahalilov, Nigorakhon Nasimova
    14th IEEE International Conference on Application of Information and Communication Technologies Aict 2020 Proceedings, 2020
  • Localization and Classification of Myocardial Infarction Based on Artificial Neural Network
    Bakhodir Muminov, Rashid Nasimov, Sanjar Mirzahalilov, Nargiza Sayfullaeva, Nigora Gadoyboyeva
    2020 Information Communication Technologies Conference Ictc 2020, 2020
  • Estimation affects of formats and resizing process to the accuracy of convolutional neural network
    Muminov Bakhodir Boltaevich, Nasimov Rashid Hamid ogli, Gadoyboyeva Nigora Soibjon qizi, Mirzahalilov Sanjar Serkabay ogli
    International Conference on Information Science and Communications Technologies Applications Trends and Opportunities Icisct 2019, 2019

RECENT SCHOLAR PUBLICATIONS

  • A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population
    A Abdusalomov, S Umirzakova, S Mirzakhalilov, A Kutlimuratov, ...
    Bioengineering 12 (10), 1066 , 2025
    2025
    Citations: 4
  • Lightweight early detection of knee osteoarthritis in athletes
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, O Ismailov, D Sultanov, ...
    Scientific Reports 15 (1), 31413 , 2025
    2025
    Citations: 8
  • Pixmed-enhancer: An efficient approach for medical image augmentation
    MJA Rasool, A Abdusalomov, A Kutlimuratov, MJA Ahamed, ...
    Bioengineering 12 (3), 235 , 2025
    2025
    Citations: 6
  • Optimized lightweight architecture for coronary artery disease classification in medical imaging
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, I Kalandarov, ...
    Diagnostics 15 (4), 446 , 2025
    2025
    Citations: 9
  • Lightweight deep learning framework for accurate detection of sports-related bone fractures
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, O Ismailov, D Sultanov, ...
    Diagnostics 15 (3), 271 , 2025
    2025
    Citations: 18
  • Accessible AI diagnostics and lightweight brain tumor detection on medical edge devices
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, A Shavkatovich Buriboev, ...
    Bioengineering 12 (1), 62 , 2025
    2025
    Citations: 31
  • RAQAMLI RIVOJLANISH VA YASHIL IQTISODIYOT: OʻZBEKISTONDA INNOVATSION TEXNOLOGIYALARNING EKOLOGIK BARQARORLIKKA TA’SIRI
    MMM O‘G‘Li, MSS O‘G‘Li
    Raqamli iqtisodiyot (Цифровая экономика), 506-516 , 2025
    2025
    Citations: 1
  • Dynamic focus on tumor boundaries: A lightweight u-net for mri brain tumor segmentation
    K Avazov, S Mirzakhalilov, S Umirzakova, A Abdusalomov, YI Cho
    Bioengineering 11 (12), 1302 , 2024
    2024
    Citations: 38
  • Gan-based novel approach for generating synthetic medical tabular data
    R Nasimov, N Nasimova, S Mirzakhalilov, G Tokdemir, M Rizwan, ...
    Bioengineering 11 (12), 1288 , 2024
    2024
    Citations: 12
  • ZAMONAVIY PROTSESSORLAR, TURLARI VA XUSUSIYATLARI
    S Mirzaxalilov, I O’ktamov
    Modern Science and Research 3 (11), 577-582 , 2024
    2024
  • ZAMONAVIY PROTSESSORLAR, TURLARI VA XUSUSIYATLARI
    MS Serkaboy o'g'li
    2024
  • Lightweight super-resolution techniques in medical imaging: Bridging quality and computational efficiency
    A Abdusalomov, S Mirzakhalilov, Z Dilnoza, K Zohirov, R Nasimov, ...
    Bioengineering 11 (12), 1179 , 2024
    2024
    Citations: 15
  • A multi-scale approach to early fire detection in smart homes
    A Abdusalomov, S Umirzakova, F Safarov, S Mirzakhalilov, ...
    Electronics 13 (22), 4354 , 2024
    2024
    Citations: 16
  • Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation. Bioengineering 2024, 11, 1302
    K Avazov, S Mirzakhalilov, S Umirzakova, A Abdusalomov, YI Cho
    2024
    Citations: 1
  • SUN’IY INTELLEKT ALGORITMLARI ASOSIDA MASHG ‘ULOT YOKI MUSOBAQALAR VAQTIDA SPORTCHINING QON BOSIMINI MASOFADAN MONITORING QILISH
    O Ismoilov, S Mirzaxalilov
    Conference on Digital Innovation:" Modern Problems and Solutions" , 2023
    2023
  • METHODOLOGICAL APPROACHES TO THE ALIGNMENT OF COORDINATION TRAINING AND PHYSICAL QUALITIES OF YOUNG SWIMMERS
    AX Mirzaxalilov
    ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ 21 (5), 14-17 , 2023
    2023
  • Primary diagnosis of cardiovascular diseases using artificial intelligence methods
    O Ismailov, M Fozilova, S Mirzakhalilov, M Ismoilov
    International scientific and practical conference" BIG DATA, artificial … , 2023
    2023
    Citations: 3
  • Research of methods and algorithms of replacation in systems with a distributed database
    OM Ismailov, S Mirzakhalilov, MO Ismoilov
    Problems of Computational and Applied Mathematics 1 (46), 116-122 , 2023
    2023
    Citations: 4
  • RESEARCH OF MODELS AND ALGORITHMS OF REMOTE MONITORING OF ARTERIAL PRESSURE
    O Ismailov, S Mirzakhalilov
    Science and innovation 2 (A7), 141-149 , 2023
    2023
  • THE IMPACT OF ARTIFICIAL INTELLIGENCE ON SPORTSMEN HEALTH: A CRITICAL ANALYSIS
    SS Mirzaxalilov
    Science and innovation 2 (Special Issue 3), 205-208 , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • Dynamic focus on tumor boundaries: A lightweight u-net for mri brain tumor segmentation
    K Avazov, S Mirzakhalilov, S Umirzakova, A Abdusalomov, YI Cho
    Bioengineering 11 (12), 1302 , 2024
    2024
    Citations: 38
  • Accessible AI diagnostics and lightweight brain tumor detection on medical edge devices
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, A Shavkatovich Buriboev, ...
    Bioengineering 12 (1), 62 , 2025
    2025
    Citations: 31
  • A new approach to classifying myocardial infarction and cardiomyopathy using deep learning
    R Nasimov, B Muminov, S Mirzahalilov, N Nasimova
    2020 international conference on information science and communications … , 2020
    2020
    Citations: 26
  • Modeling and applying implicit dormant features for recommendation via clustering and deep factorization
    A Kutlimuratov, AB Abdusalomov, R Oteniyazov, S Mirzakhalilov, ...
    Sensors 22 (21), 8224 , 2022
    2022
    Citations: 25
  • Localization and classification of myocardial infarction based on artificial neural network
    B Muminov, R Nasimov, S Mirzahalilov, N Sayfullaeva, N Gadoyboyeva
    2020 Information Communication Technologies Conference (ICTC), 245-249 , 2020
    2020
    Citations: 22
  • Lightweight deep learning framework for accurate detection of sports-related bone fractures
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, O Ismailov, D Sultanov, ...
    Diagnostics 15 (3), 271 , 2025
    2025
    Citations: 18
  • A multi-scale approach to early fire detection in smart homes
    A Abdusalomov, S Umirzakova, F Safarov, S Mirzakhalilov, ...
    Electronics 13 (22), 4354 , 2024
    2024
    Citations: 16
  • Algorithm of automatic differentiation of myocardial infarction from cardiomyopathy based on electrocardiogram
    R Nasimov, B Muminov, S Mirzahalilov, N Nasimova
    2020 IEEE 14th International Conference on Application of Information and … , 2020
    2020
    Citations: 16
  • Lightweight super-resolution techniques in medical imaging: Bridging quality and computational efficiency
    A Abdusalomov, S Mirzakhalilov, Z Dilnoza, K Zohirov, R Nasimov, ...
    Bioengineering 11 (12), 1179 , 2024
    2024
    Citations: 15
  • Comparative analysis of the results of EMG signal classification based on machine learning algorithms
    A Turgunov, K Zohirov, R Nasimov, S Mirzakhalilov
    2021 International Conference on Information Science and Communications … , 2021
    2021
    Citations: 14
  • Gan-based novel approach for generating synthetic medical tabular data
    R Nasimov, N Nasimova, S Mirzakhalilov, G Tokdemir, M Rizwan, ...
    Bioengineering 11 (12), 1288 , 2024
    2024
    Citations: 12
  • Optimized lightweight architecture for coronary artery disease classification in medical imaging
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, I Kalandarov, ...
    Diagnostics 15 (4), 446 , 2025
    2025
    Citations: 9
  • Методы применения алгоритмов искуственного интеллекта в мониторинге здоровья спортсменов во время тренировок и соревнований
    ОМ Исмаилов, СС Мирзахалилов, ГН Холдарова
    Science and innovation 2 (Special Issue 3), 697-702 , 2023
    2023
    Citations: 9
  • Lightweight early detection of knee osteoarthritis in athletes
    A Abdusalomov, S Mirzakhalilov, S Umirzakova, O Ismailov, D Sultanov, ...
    Scientific Reports 15 (1), 31413 , 2025
    2025
    Citations: 8
  • Исследование методов и алгоритмов репликации в системах с распределенной базой данных
    ОМ Исмаилов, С Мирзахалилов, МО Исмаилов
    Проблемы вычислительной и прикладной математики, 46 , 2023
    2023
    Citations: 7
  • Localization and Classification of Myocardial Infarction Based on Artificial Neural Network,(2020) 2020 Information Communication Technologies Conference
    B Muminov, R Nasimov, S Mirzahalilov, N Sayfullaeva, N Gadoyboyeva
    ICTC , 2020
    2020
    Citations: 7
  • Pixmed-enhancer: An efficient approach for medical image augmentation
    MJA Rasool, A Abdusalomov, A Kutlimuratov, MJA Ahamed, ...
    Bioengineering 12 (3), 235 , 2025
    2025
    Citations: 6
  • Nasimov Rashid Hamid ogli
    MB Boltaevich
    Gadoyboyeva Nigora Soibjon qizi and Mirzahalilov Sanjar Serkabay ogli … , 2019
    2019
    Citations: 6
  • A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population
    A Abdusalomov, S Umirzakova, S Mirzakhalilov, A Kutlimuratov, ...
    Bioengineering 12 (10), 1066 , 2025
    2025
    Citations: 4
  • Research of methods and algorithms of replacation in systems with a distributed database
    OM Ismailov, S Mirzakhalilov, MO Ismoilov
    Problems of Computational and Applied Mathematics 1 (46), 116-122 , 2023
    2023
    Citations: 4