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A lightweight attention-driven YOLOv5m model for improved brain tumor detection

Computers in Biology and Medicine 2025年 188卷 109893-109893页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Mardieva, Sevara Iskhakova, Nargiza Sultanov, Murodjon Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Seongnam-si461-701 Korea Republic of Department of Information Systems and Technologies of the Tashkent State University of Economic Tashkent100066 Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error. Our innovative approach leverages the ESA layer to acutely focus on salient features, significantly improving the method ability to differentiate between common classes of brain tumors—meningioma, pituitary, and glioma tumors. By processing spatial features with enhanced precision, the model minimizes false positives and maximizes detection reliability. Validated against a comprehensive dataset of 3064 T1-weighted contrast-enhanced MRI images from 233 patients, our modified YOLOv5m architecture demonstrates superior performance metrics compared to the standard model, highlighting its potential as a robust tool in clinical applications for automated and precise brain tumor diagnosis. © 2025 The Authors

关键词： Dynamic contrast enhanced MRI

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Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI

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Computers in Biology and Medicine 2025年 190卷 110095-110095页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Iskhakova, Nargiza Khaitov, Aziz Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Gyeonggi-do Seongnam-si461-701 Korea Republic of Department of Systematic and Practical Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan Republican Scientific and Methodological Center for the Development of Education of the Republic of Uzbekistan Uzbekistan

This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Magnetic Resonance Imaging (MRI). The model architecture is meticulously crafted to enhance feature extraction and selectively focus on crucial regions within brain images, thereby improving diagnostic accuracy. A unique component, the MRI Segmentation Block (MSB), is introduced to manage the skull stripping task effectively, highlighting the model ability to learn from complex, multilayered information. We have incorporated a detailed experimental evaluation of the MSB, demonstrating its superior performance in cranial debridement tasks compared to existing methods. The experiments involved a range of MRI scans, assessing the MSB's accuracy through metrics like the Dice Coefficient and Jaccard Index against ground truth annotations by expert radiologists. The results substantiate the MSB's effectiveness, setting a new benchmark for precision in medical imaging diagnostics. The proposed method integrates densely connected neural networks with a connection-wise attention model to extract multiscale features from MRI scans. Furthermore, the attention mechanism is fine-tuned to emphasize salient features significantly associated with various stages of Alzheimer's disease, thereby setting a new benchmark for precision in medical imaging diagnostics. Extensive experiments on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset demonstrate the superiority of our method over traditional and contemporary approaches, with our model achieving high accuracy and computational efficiency. This makes it suitable for clinical applications where resources are limited. This study represents a significant advancement in the diagnostic process for AD, with potential implications for improving patient outcomes in clinical settings. © 2025 The Authors

关键词： Convolutional neural networks

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Classification Algorithms with a Complex Structure in Pattern Recognition 25

Classification Algorithms with a Complex Structure in Patter...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Nishanov, Akram Kh. Allamov, Oybek Rakhmanov, Askar T. Yusupova, Janar Ruzibaev, Ortik B. Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.

关键词： Consensus algorithm

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Classification Algorithms with a Complex Structure in Pattern Recognition

Classification Algorithms with a Complex Structure in Patter...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Akram Kh. Nishanov Oybek Allamov Askar T. Rakhmanov Janar Yusupova Ortik B. Ruzibaev Department of Systematic and Practical Programming Technical Sciences Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of systematic and practical programming Tashkent university of information technologies named after muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

关键词： Training Productivity Measurement units Atmospheric measurements Symbols Particle measurements Classification algorithms

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An approach to finding the most optimal route in a dynamic graph

An approach to finding the most optimal route in a dynamic g...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Nishanov, Akhram Khasanovich Allamov, Oybek Turabaevich Ruzibaev, Ortiq Bakhtiyorovich Abdullaev, Anvar Shukhratbekovich Allamova, Shakhlo Turabaevna Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Systematic and Practical Programming Tashkent Uzbekistan Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Software Engineering Khorezm region Urgench Uzbekistan Software Enginering Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Urgench Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Engineering Khorezm region Urgench Uzbekistan

ISBN: (纸本)9781665432580

The article proposes an algorithm for determining the optimal ways for users to travel from one place to another. Traffic jams occur in cities as a result of road repairs and car accidents. As a result, the shortest route planned for the trip can become a useless route. Given these circumstances, the article presents a parallel processing method distributed on a large-scale graph to control traffic. As a result, an algorithm for parallel and distributed processing of large-volume graphs was developed and tested for large-volume data. The test algorithm is illustrated with a wide range of graphs. Experimental experiments of the algorithm were conducted on the basis of data from the city of Urgench, Uzbekistan. © 2021 IEEE.

关键词： Smart city

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Methodology of Teaching programming Science Through Online Platforms 3

Methodology of Teaching Programming Science Through Online P...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Nishanov, Akram Allamov, Oybek Yusupova, Janar Matyakubov, Marks Qalandarov, Baxtiyor Bahrom, Reyimberganov Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Systematic and Practical Programming Tashkent Uzbekistan Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Department of Software Engineering Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench branch of Tatu named after Muhammad al-Khwarizmi Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench branch of Tatu named after Muhammad al-Khwarizmi Urgench Uzbekistan

ISBN: (纸本)9798331516321

This article presents an experiment conducted among students of the Faculty of 'Software Engineering' of the Urgench branch of TATU named after Muhammad al-Khwarizmi. Teaching programming using online platforms in education has shown promising results in improving student outcomes. This article discusses the methodology used, the comparative results, and the impact of the platform on software education. The rapid adoption of online platforms in education has changed the way programming is taught, providing students with an interactive, hands-on learning environment. This shift is especially relevant in teaching programming, where real-time feedback and practice are essential to mastering algorithms and coding concepts. The study compared two groups: one was trained using the https://***/platform and the assignments were checked using this system, and the second group was trained using traditional methods. The results showed that students who used the online platform had a high level of mastery, which highlighted the effectiveness of integrating https://***/into programming education. © 2024 IEEE.

关键词： Students

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Methodology of Teaching programming Science Through Online Platforms

Methodology of Teaching Programming Science Through Online P...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Akram Nishanov Oybek Allamov Janar Yusupova Marks Matyakubov Baxtiyor Qalandarov Reyimberganov Bahrom Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan Master of Urgench branch of TATU named after Muhammad al-Khwarizmi Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan Student of Urgench branch of TATU named after Muhammad al-Khwarizmi Urgench Branch of Tashkent University of Information Technology named after Muhammad al-Khwarizmi Urgench Uzbekistan

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This article presents an experiment conducted among students of the Faculty of “Software Engineering” of the Urgench branch of TATU named after Muhammad al-Khwarizmi. Teaching programming using online platforms in education has shown promising results in improving student outcomes. This article discusses the methodology used, the comparative results, and the impact of the platform on software education. The rapid adoption of online platforms in education has changed the way programming is taught, providing students with an interactive, hands-on learning environment. This shift is especially relevant in teaching programming, where real-time feedback and practice are essential to mastering algorithms and coding concepts. The study compared two groups: one was trained using the https://***/platform and the assignments were checked using this system, and the second group was trained using traditional methods. The results showed that students who used the online platform had a high level of mastery, which highlighted the effectiveness of integrating https://***/into programming education.

关键词： Education Software algorithms Real-time systems Software Encoding Computer science education Problem-solving Informatics programming profession

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