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001-es BibID:BIBFORM086224
035-os BibID:(WoS)000582418600040 (Scopus)85085570624
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:Multiple Sclerosis Detection via Machine Learning Algorithm, Accurate Simulated Database 3D MRI to 2D Images, using value of Binary Pattern Classification : A Case Study / Mohammad Moghadasi, Gabor Fazekas
Megjegyzések:This paper is aiming to review some machine learning tools and to see if support vector machines (SVM) are accurate in 3D MRI images. We intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. According many research and database, 3D images are more informative than 2D ones, however working with 3D images is time consuming and requires specific programs and coding. In this research work and next which case study is under progress, we try to see the benefits of having a 3D database but to use 2D vectors only for comparison and more accurate results. This case study helps to gain better outcome. Support vector machines (SVM) can be a useful tool during the diagnosis process, however to be able to make better assumptions, more tests are needed. The technology can be viewed as detailed because it is usually applied in the discrimination of the blocks found in the areas of MS lesions and the regions which are not affected by the lesions. Primarily, to correctly segregate the different areas the textural background plays a crucial part in elevating the effectiveness of the imaging concept. In this case, the study of the slice blocks should be done comprehensively as it aims in ensuring that the type of results provided showcases the exact situation of the individual suffering from the ailment.
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
Megjelenés:Proceedings of the 10th IEEE International Conference on Cognitive Infocommunications : CogInfoCom 2019 / szerk. Péter Baranyi. - p. 233-240. -
További szerzők:Fazekas Gábor (1952-) (informatikus, matematikus)
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