Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Abdusalomov, Akmalbek Bobomirzaevich; Nasimov, Rashid; Nasimova, Nigorakhon; Muminov, Bahodir; Whangbo, Taeg Keun

doi:10.3390/s23073440

Cited by 29 publications

(8 citation statements)

References 54 publications

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“…F1 score is calculated as the harmonic mean of the precision and recall values [ 33 ], which indicates better target detection accuracy [ 34 ]. The F1 score ranges between 0 and 1, with a higher value indicating better model performance, as detailed in these papers [ 35 , 36 , 37 , 38 , 39 , 40 ].

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Section: Resultsmentioning

confidence: 99%

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Avazov

Jamil

Muminov

et al. 2023

Sensors

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Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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…”

Section: Resultsmentioning

confidence: 99%

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Avazov

Jamil

Muminov

et al. 2023

Sensors

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“…Now, if we turn to the issue of evaluating the quality of the developed method, it is known that today various methods have been developed for the evaluation of synthetic data. However, some are designed to evaluate synthetic images [ 33 ], others to determine the level of security [ 34 ], and others to evaluate the difference between the distributions of synthetic and real images [ 35 ]. However, none of the above methods were suitable for evaluating the proposed method.…”

Section: Resultsmentioning

confidence: 99%

New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

Tagmatova,

Abdusalomov,

Nasimov

et al. 2023

Bioengineering

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The lack of medical databases is currently the main barrier to the development of artificial intelligence-based algorithms in medicine. This issue can be partially resolved by developing a reliable high-quality synthetic database. In this study, an easy and reliable method for developing a synthetic medical database based only on statistical data is proposed. This method changes the primary database developed based on statistical data using a special shuffle algorithm to achieve a satisfactory result and evaluates the resulting dataset using a neural network. Using the proposed method, a database was developed to predict the risk of developing type 2 diabetes 5 years in advance. This dataset consisted of data from 172,290 patients. The prediction accuracy reached 94.45% during neural network training of the dataset.

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“…Leveraging these advanced artificial intelligence methods could enable robust, automatic sarcopenia quantification and staging from routinely acquired CT, MRI, or ultrasound scans. This would facilitate large-scale screening, enhance reliability compared to manual evaluation, and reduce costs [ 116 , 117 , 118 , 119 , 120 ]. Further research should explore fuzzy logic and deep learning for sarcopenia diagnosis and validate these techniques on diverse, clinically relevant datasets.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Turimov Mustapoevich,

Kim

2023

Healthcare

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This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

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Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Cited by 29 publications

References 54 publications

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

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