Fuzzy-Based Framework for the Selection of Image Processing Software for Diagnosis and Outcome Prediction of Cardiac Diseases

Rajah, Tanisha Thasa Ratha; Chiam, Yin Kia; Hasan, Zati Hakim Azizul

doi:10.1166/asl.2018.10697

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…After the reconstruction is completed, the data will be directly transmitted to the workstation, and then, the radiologist will dock with the reconstruction sequence on the workstation in real time. Most workstations with cardiopulmonary analysis functions can automatically orient the volumetric image dataset along the heart axis to the traditionally used heart plane (i.e., short axis, horizontal long axis, vertical long axis) by clicking a button [18,19]. This function is particularly useful for evaluating the function of the heart cavity and lungs.…”

Section: Application Methods Of Image Diagnosis Technology In Medical mentioning

confidence: 99%

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

Zhang

Liu

Shao

et al. 2021

Pers Ubiquit Comput

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The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

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Section: Application Methods Of Image Diagnosis Technology In Medical mentioning

confidence: 99%

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

Zhang

Liu

Shao

et al. 2021

Pers Ubiquit Comput

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“…The boiler is one of the three major devices of the thermal power plant. The single-unit capacity of the boilers in power plants is constantly rising, and the boilers are developing toward large capacity and high parameters [4,5]. For large or very large coal-fired boilers (supercritical units), if the combustion instability occurs during combustion, the thermal efficiency of boiler combustion will directly decrease, and a large number of pollution by-products will be generated, even some extreme situations may occur, such as furnace cavity extinction and major furnace safety accidents [6,7].…”

Section: Introductionmentioning

confidence: 99%

Computer image processing and neural network technology for thermal energy diagnosis of boiler plants

Zhang

2020

THERM SCI

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The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.

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Fuzzy-Based Framework for the Selection of Image Processing Software for Diagnosis and Outcome Prediction of Cardiac Diseases

Cited by 2 publications

References 11 publications

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

Computer image processing and neural network technology for thermal energy diagnosis of boiler plants

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