EFF_D_SVM: a robust multi-type brain tumor classification system Article Swipe
Jincan Zhang
,
Xinghua Tan
,
Wenna Chen
,
Ganqin Du
,
Qizhi Fu
,
Hongri Zhang
,
Hongwei� Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2023.1269100
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2023.1269100
Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2023.1269100
- https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 10
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387213005
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387213005Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2023.1269100Digital Object Identifier
- Title
-
EFF_D_SVM: a robust multi-type brain tumor classification systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-29Full publication date if available
- Authors
-
Jincan Zhang, Xinghua Tan, Wenna Chen, Ganqin Du, Qizhi Fu, Hongri Zhang, Hongwei� JiangList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2023.1269100Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/pdf?isPublishedV2=FalseDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
-
Support vector machine, Computer science, Artificial intelligence, Brain tumor, Softmax function, Pattern recognition (psychology), Convolutional neural network, Robustness (evolution), Machine learning, Medicine, Pathology, Chemistry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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