Otabek Ismailov

@tuit.uz

Professor

Otabek Ismailov


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https://researchid.co/otabekismailov
3

Scopus Publications

Scopus Publications

  • Lightweight early detection of knee osteoarthritis in athletes
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Otabek Ismailov, Djamshid Sultanov, Rashid Nasimov, Young Im Cho
    Scientific Reports, 2025
    Osteoarthritis (OA) is a prevalent condition among athletes, characterized by the progressive degradation of joint cartilage, particularly in weight-bearing joints such as the knees. Early detection is critical for effective management and prevention of long-term complications. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in medical diagnostics. In this study, we propose a novel approach for early-stage OA detection using an optimized EfficientNet-B0 architecture enhanced with the Efficient Channel Attention (ECA) module. This integration addresses the limitations of traditional attention mechanisms, such as Squeeze-and-Excitation (SE) blocks, by providing lightweight and computationally efficient feature recalibration. Our methodology is evaluated using the Knee Osteoarthritis Severity Grading Dataset, focusing on binary classification between healthy and early-stage OA cases. Comprehensive experiments demonstrate that the proposed model achieves superior accuracy, precision, and recall compared to baseline and State-of-the-Art (SOTA) architectures, including ResNet-50, VGG-16, and DenseNet, while maintaining minimal computational overhead. Class Activation Maps (CAMs) further validate the model capability to localize clinically relevant features, such as joint space narrowing and osteophyte formation, indicative of OA progression. This research not only sets a new benchmark for automated OA diagnostics but also emphasizes the importance of balancing high performance with resource efficiency. The proposed model lightweight architecture and robust diagnostic capabilities make it a strong candidate for real-time clinical applications, paving the way for improved patient outcomes through early intervention.
  • Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures
    Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova, Otabek Ismailov, Djamshid Sultanov, Rashid Nasimov, Young-Im Cho
    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.
  • Tooth Square Detection Using Artificial Intelligence
    Otabek Ismailov, Xosiyat Temirova
    Aip Conference Proceedings, 2024