JOURNAL DESCRIPTION
The Medical Radiology and Radiation Safety journal ISSN 1024-6177 was founded in January 1956 (before December 30, 1993 it was entitled Medical Radiology, ISSN 0025-8334). In 2018, the journal received Online ISSN: 2618-9615 and was registered as an electronic online publication in Roskomnadzor on March 29, 2018. It publishes original research articles which cover questions of radiobiology, radiation medicine, radiation safety, radiation therapy, nuclear medicine and scientific reviews. In general the journal has more than 30 headings and it is of interest for specialists working in thefields of medicine¸ radiation biology, epidemiology, medical physics and technology. Since July 01, 2008 the journal has been published by State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency. The founder from 1956 to the present time is the Ministry of Health of the Russian Federation, and from 2008 to the present time is the Federal Medical Biological Agency.
Members of the editorial board are scientists specializing in the field of radiation biology and medicine, radiation protection, radiation epidemiology, radiation oncology, radiation diagnostics and therapy, nuclear medicine and medical physics. The editorial board consists of academicians (members of the Russian Academy of Science (RAS)), the full member of Academy of Medical Sciences of the Republic of Armenia, corresponding members of the RAS, Doctors of Medicine, professor, candidates and doctors of biological, physical mathematics and engineering sciences. The editorial board is constantly replenished by experts who work in the CIS and foreign countries.
Six issues of the journal are published per year, the volume is 13.5 conventional printed sheets, 88 printer’s sheets, 1.000 copies. The journal has an identical full-text electronic version, which, simultaneously with the printed version and color drawings, is posted on the sites of the Scientific Electronic Library (SEL) and the journal's website. The journal is distributed through the Rospechat Agency under the contract № 7407 of June 16, 2006, through individual buyers and commercial structures. The publication of articles is free.
The journal is included in the List of Russian Reviewed Scientific Journals of the Higher Attestation Commission. Since 2008 the journal has been available on the Internet and indexed in the RISC database which is placed on Web of Science. Since February 2nd, 2018, the journal "Medical Radiology and Radiation Safety" has been indexed in the SCOPUS abstract and citation database.
Brief electronic versions of the Journal have been publicly available since 2005 on the website of the Medical Radiology and Radiation Safety Journal: http://www.medradiol.ru. Since 2011, all issues of the journal as a whole are publicly available, and since 2016 - full-text versions of scientific articles. Since 2005, subscribers can purchase full versions of other articles of any issue only through the National Electronic Library. The editor of the Medical Radiology and Radiation Safety Journal in accordance with the National Electronic Library agreement has been providing the Library with all its production since 2005 until now.
The main working language of the journal is Russian, an additional language is English, which is used to write titles of articles, information about authors, annotations, key words, a list of literature.
Since 2017 the journal Medical Radiology and Radiation Safety has switched to digital identification of publications, assigning to each article the identifier of the digital object (DOI), which greatly accelerated the search for the location of the article on the Internet. In future it is planned to publish the English-language version of the journal Medical Radiology and Radiation Safety for its development. In order to obtain information about the publication activity of the journal in March 2015, a counter of readers' references to the materials posted on the site from 2005 to the present which is placed on the journal's website. During 2015 - 2016 years on average there were no more than 100-170 handlings per day. Publication of a number of articles, as well as electronic versions of profile monographs and collections in the public domain, dramatically increased the number of handlings to the journal's website to 500 - 800 per day, and the total number of visits to the site at the end of 2017 was more than 230.000.
The two-year impact factor of RISC, according to data for 2017, was 0.439, taking into account citation from all sources - 0.570, and the five-year impact factor of RISC - 0.352.
Медицинская радиология и радиационная безопасность. 2024. Том 69. № 3
DOI:10.33266/1024-6177-2024-69-3-81-85
Муайед Ф. Аль-Рави, Изз К. Аббуд и Насир А. Аль-Авад
НОВЫЙ ПОДХОД НА ОСНОВЕ ТРАНСФЕРНОГО ГЛУБОКОГО ОБУЧЕНИЯ ДЛЯ ПРОГНОЗИРОВАНИЯ ОПУХОЛЕЙ ГОЛОВНОГО МОЗГА
Инженерный колледж Университета Мустансирия, Багдад, Ирак
Контактное лицо: Муайед Ф. Аль-Рави, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
РЕФЕРАТ
Опухоль головного мозга – это аномальное скопление клеток в головном мозге, которое потенциально может представлять угрозу для жизни из-за способности клеток проникать в близлежащие органы и давать метастазы. Правильно диагностировав это потенциально смертельное заболевание, можно спасти жизни. За последние несколько лет функциональность приложений глубокого обучения при автоматическом распознавании МРТ-изображений опухолей головного мозга заметно расширилась. В результате усовершенствование архитектуры модуля приводит к более точному отображению отслеживаемой конфигурации. Благодаря предоставлению надежных наборов данных, в классификации опухолей с помощью алгоритмов глубокого обучения был достигнут значительный прогресс. Цель статьи – использовать алгоритмы модуля переноса для прогнозирования опухолей головного мозга. К таким модулям относятся MobileNet, VGG19, InceptionResnetV2, Inception и DenseNet201. В предлагаемом модуле используются три основных оптимизатора: Adam, SGD и RMSProp. Результаты моделирования показывают, что предварительно обученный модуль MobileNet с оптимизатором RMSProp превзошел все другие оцененные модули. В дополнение к минимальному времени, затрачиваемому на вычисления, он обеспечил точность в 99,6 %, чувствительность в 99,4 % и специфичность в 100 %.
Ключевые слова: медицинские изображения, опухоль головного мозга, автоматическое распознавание, машинное и глубокое обучение, компьютерное зрение, МРТ
Для цитирования: Муайед Ф. Аль-Рави, Изз К. Аббуд и Насир А. Аль-Авад. Новый подход на основе трансферного глубокого обучения для прогнозирования опухолей головного мозга // Медицинская радиология и радиационная безопасность. 2024. Т. 69.
№ 3. С. 81–85. DOI:10.33266/1024-6177-2024-69-3-81-85
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Конфликт интересов. Авторы заявляют об отсутствии конфликта интересов.
Финансирование. Исследование не имело спонсорской поддержки.
Участие авторов. Cтатья подготовлена с равным участием авторов.
Поступила: 20.01.2024. Принята к публикации: 27.02.2024.