Volume 8, Issue 2 (9-2021)
jhbmi 2021, 8(2): 218-233 |
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Khazaei Z, Langarizadeh M, Shiri Ahmad Abadi M E. Glioma Brain Tumor Identification Using Magnetic Resonance Imaging with Deep Learning Methods: A Systematic Review. jhbmi 2021; 8 (2) :218-233
URL: http://jhbmi.ir/article-1-565-en.html
URL: http://jhbmi.ir/article-1-565-en.html
Ph.D. in Medical Informatics, Associate Professor, Department of Health Information Management, School of Health Management and Information Sciences ,Iran University of Medical Sciences, Tehran, Iran
Abstract: (2116 Views)
Introduction: Glioma is one of the most common brain tumors, the early and accurate diagnosis of which leads to proper treatment and prolongs the patient’s life. The studies conducted on glioma diagnosis using magnetic resonance imaging images with deep learning methods were reviewed and analyzed in this study.
Method: This study was a systematic review in which PubMed, ScienceDirect, Springer, IEEE, and Arxiv databases were searched between 2010 and 2020 in order to retrieve English language studies using keywords. Then, the articles were selected based on the inclusion and exclusion criteria and in line with the purpose of the research and the required information was extracted for review.
Results: Finally, 35 original research articles were selected. The review of the articles showed that they used a pipeline including collecting images, preprocessing, designing and implementing a model, and evaluating the results of the model for tumor detection, classification, and segmentation. The majority of the articles used public images and pre-trained models. In most articles, Dice similarity coefficient and accuracy criteria were used in segmentation and classification, respectively, as model evaluation criteria.
Conclusion: The results of this study revealed that in most articles, segmentation received more attention in comparison with detection and classification. Therefore, it is suggested that more studies be carried out on detection and especially grading glioma for being utilized in medical diagnostic assistance systems.
Method: This study was a systematic review in which PubMed, ScienceDirect, Springer, IEEE, and Arxiv databases were searched between 2010 and 2020 in order to retrieve English language studies using keywords. Then, the articles were selected based on the inclusion and exclusion criteria and in line with the purpose of the research and the required information was extracted for review.
Results: Finally, 35 original research articles were selected. The review of the articles showed that they used a pipeline including collecting images, preprocessing, designing and implementing a model, and evaluating the results of the model for tumor detection, classification, and segmentation. The majority of the articles used public images and pre-trained models. In most articles, Dice similarity coefficient and accuracy criteria were used in segmentation and classification, respectively, as model evaluation criteria.
Conclusion: The results of this study revealed that in most articles, segmentation received more attention in comparison with detection and classification. Therefore, it is suggested that more studies be carried out on detection and especially grading glioma for being utilized in medical diagnostic assistance systems.
Type of Study: Systematic Review & Meta analysis |
Subject:
Artificial Intelligence in Healthcare
Received: 2021/02/23 | Accepted: 2021/04/3
Received: 2021/02/23 | Accepted: 2021/04/3
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