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.
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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. 2024. Vol. 69. № 3
DOI:10.33266/1024-6177-2024-69-3-81-85
Muaayed F. Al-Rawi, Izz K. Abboud, and Nasir A. Al-Awad
Novel Approach Using Transfer Deep Learning for Brain Tumor Prediction
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:
A brain tumor refers to an abnormal collection or aggregation of cells in the brain that has the potential to be life-threatening owing to the cells’ capacity to penetrate and metastasize to organs that are nearby. It is possible to save lives by making a correct diagnosis of this potentially fatal condition. Within the last several years, there has been a noticeable increase in the functionality of deep learning applications. As a result, improving the module’s architecture leads to better approximations in the monitored configuration. Through the provision of trustworthy datasets, the categorization of tumors via the use of deep learning algorithms has successfully achieved significant progress. The purpose of this article is to use transfer module algorithms for the prediction of brain tumors. These modules include MobileNet, VGG19, InceptionResNetV2, Inception, and DenseNet201. The suggested module uses three main optimizers: Adam, SGD, and RMSprop. The simulation findings indicate that the pre-trained MobileNet module with the RMSprop optimizer outperformed all other evaluated modules. In addition to having the shortest amount of time required for computing, it obtained an accuracy of 99.6 %, a sensitivity of 99.4 %, and a specificity of 100 %.
Keywords: medical images, brain tumor, machine and deep learning, computer vision, MRI
For citation: Muaayed F. Al-Rawi, Izz K. Abboud, and Nasir A. Al-Awad. Novel Approach Using Transfer Deep Learning for Brain Tumor Prediction. Medical Radiology and Radiation Safety. 2024;69(3):81–85. (In Russian). DOI:10.33266/1024-6177-2024-69-3-81-85
References
1. Mzoughi H., et al. Deep Multi-Scale 3d Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classifcation. J. Digit. Imaging. 2020;33;903–915.
2. Muhammad Sjjad, Salman Khan, Khan Muhammad, Wanqing Wu, Amin Ullah, Sung Wook Baik. Multigrade Brain Tumor Classification Using Deep CNN with Extensive Data Augmentation. Elsevier. Journal of Computational Science. 2019;30:174-182.
3. Amin Kabir Anaraki, Moosa Ayati, Foad Kazemi. Magnetic Resonance Imaging-Based Brain Tumor Grades Classification and Grading Via Convolutional Neural Networks and Genetic Algorithms. Elsevier. Biocybergenetics and Biomedical Engineering. 2019;39:63-74.
4. Deepak P.M. Ameer. Brain Tumor Classification Using Deep CNN Features Via Transfer Learning. Elsevier. Computers in Biology and Medicine. 2019;111:1-7.
5. R.Vimal Kurup, V.Sowmya, K.P.Soman. Effect of Data Pre-processing on Brain Tumor Classification Using Capsulenet. Springer. ICICCT System Reliability, Quality Control, Safety, Maintenance and Management. 2019:110-119.
6. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir, Farman Ali, Zakir Ali, Saeed Ahmed, Jianfeng Lu. Brain Tumor Classification for MR Images Using Transfer Learning and Finetuning. Elsevier. Computerized Medical Imaging and Graphics. 2019;75:34-46.
7. Nyoman Abiwinanda, Muhammad Hanif, S. TafwidaHesaputra, Astri Handayani, Tati Rajab Mengko. Brain Tumor Classification Using Convolutional Neural Network. Springer. World Congress on Medical Physics and Biomedical Engineering. 2018:183-189.
8. F.P.Polly, S.K.Shil, M.A.Hossain, A.Ayman, Y.M.Jang. Detection and Classification of HGG and LGG Brain Tumor Using Machine Learning. IEEE. International Conference on Information Networking (ICOIN), 2018.
9. Heba Mohsen, El-Sayed A.El-Dahshan, El-Sayed M.El-Horbaty, Abdel-Badeeh M.Salem. Classification Using Deep Learning Neural Networks for Brain Tumors. Elsevier. Future Computing and Informatics Journal. 2018;3:68-71.
10. Garima Singh, Dr M.A.Ansari. Efficient Detection of Brain Tumor from MRIs Using K-Means Segmentation and Normalized Histogram. IEEE. 1st India International Conference on Information Processing (IICIP), 2016.
11. Parnian Afshar, Konstantinos N. Plantaniotis, Arash Mohammadi. Capsule Networks for Brain Tumor Classification Based on MRI Images Coarse Tumor Boundaries. IEEE. International Conference on Acoustics, Speech and Signal Processing, 2019.
12. https://www.kaggle.com/datasets/ahmedhamada0/braintumor-detection.
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: 20.01.2024. Accepted for publication: 27.02.2024.