Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.
چکیده
کشورعراق مرزهای زمینی ایران دارد که علاوه بر این ، مشترکات وروابط متعدد اقتصادی ،سیاسی، فرهنگی ودينى ميان هر دو ملت می باشند . عراق بعلت موقعیت جغرافیایی ونزدیکی به ایران سهم بیشتری برای ورود واژه های فارسی به لهجه ی خود داشت ، علاوه بر این رویدادهای سیاسی متقابل قدیم و جدید میان دو کشور سبب ورود بسیاری از لغات فارسی به لهجه ی عراقی شد که کاربرد این لغات هم اکنون در لهجه ی عراقی آشکار است ، ای
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