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.
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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.
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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.
Medical Radiology and Radiation Safety. 2025. Vol. 70. № 3
DOI:10.33266/1024-6177-2025-70-3-83-89
Muaayed F. Al-Rawi, Izz K. Abboud, Nasir A. Al-Awad
Using Machine Learning Algorithms to Detect Cancer Automatically
College of Engineering, Mustansiriyah University, Baghdad, Iraq
Contact person: Muaayed F. Al-Rawi, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The number of people diagnosed with cancer is growing all around the world. During the last twenty years, the overall cancer incidence in Iraq has doubled, leading to an increase in the number of diagnosed cancer fatalities. When it comes to deaths that occur in hospitals, cancer is the second-biggest cause. Therefore, a remedy to the issue should be an arrangement to decrease time waste, the right technique of directing the patient to notice symptoms, extremely accurate cancer detection, and a better monitoring system. The proposed method is an arrangement that lets and leads a patient to identify symptoms on their own, guiding them to a proper healthcare professional, correctly diagnosing cancer in its initial stages, and monitoring the patient throughout therapy. Currently, research into cancer detection systems only employs a single machine learning approach to identify cancer. The study that is being presented makes use of Convolutional Neural Networks (CNN), Random Forest, and the XGBoost Classifier, which are a machine learning algorithms that are applied to structured and tabular data in order to identify the existence of breast cancer, brain tumors, skin cancer, and lung cancer. These methods provide findings more quickly while also achieving a greater level of accuracy. Hosting this suggested solution in the cloud with a cutting-edge program will make it available to the public, providing an improved user experience and easier operation.
Keywords: radiation diagnostics, machine learning, CNN, Random Forest, XGBoost classifier, Cancer detection, Brain cancer, Skin cancer, Lung cancer
For citation: Al-Rawi Muaayed F., Abboud Izz K., Al-Awad Nasir A. Using Machine Learning Algorithms to Detect Cancer Automatically. Medical Radiology and Radiation Safety. 2025;70(3):83–89. DOI:10.33266/1024-6177-2025-70-3-83-89
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PDF (RUS) Full-text article (in Russian)
Conflict of interest. The authors declare no conflict of interest.
Financing. The study had no sponsorship.
Contribution. Article was prepared with equal participation of the authors.
Article received: 20.02.2025. Accepted for publication: 25.03.2025.