EFF_D_SVM: a robust multi-type brain tumor classification system
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 redu...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/full |
| _version_ | 1797670021088935936 |
|---|---|
| author | Jincan Zhang Xinghua Tan Wenna Chen Ganqin Du Qizhi Fu Hongri Zhang Hongwei Jiang |
| author_facet | Jincan Zhang Xinghua Tan Wenna Chen Ganqin Du Qizhi Fu Hongri Zhang Hongwei Jiang |
| author_sort | Jincan Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T20:53:28Z |
| format | Article |
| id | doaj.art-395bcc48ba4b4ac7abc87e8471f8eb7a |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-11T20:53:28Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-395bcc48ba4b4ac7abc87e8471f8eb7a2023-09-30T21:58:10ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-09-011710.3389/fnins.2023.12691001269100EFF_D_SVM: a robust multi-type brain tumor classification systemJincan Zhang0Xinghua Tan1Wenna Chen2Ganqin Du3Qizhi Fu4Hongri Zhang5Hongwei Jiang6College of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaThe First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaBrain 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/fullbrain tumorstransfer learningfeature extractiongrad-CAMrobustness |
| spellingShingle | Jincan Zhang Xinghua Tan Wenna Chen Ganqin Du Qizhi Fu Hongri Zhang Hongwei Jiang EFF_D_SVM: a robust multi-type brain tumor classification system Frontiers in Neuroscience brain tumors transfer learning feature extraction grad-CAM robustness |
| title | EFF_D_SVM: a robust multi-type brain tumor classification system |
| title_full | EFF_D_SVM: a robust multi-type brain tumor classification system |
| title_fullStr | EFF_D_SVM: a robust multi-type brain tumor classification system |
| title_full_unstemmed | EFF_D_SVM: a robust multi-type brain tumor classification system |
| title_short | EFF_D_SVM: a robust multi-type brain tumor classification system |
| title_sort | eff d svm a robust multi type brain tumor classification system |
| topic | brain tumors transfer learning feature extraction grad-CAM robustness |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1269100/full |
| work_keys_str_mv | AT jincanzhang effdsvmarobustmultitypebraintumorclassificationsystem AT xinghuatan effdsvmarobustmultitypebraintumorclassificationsystem AT wennachen effdsvmarobustmultitypebraintumorclassificationsystem AT ganqindu effdsvmarobustmultitypebraintumorclassificationsystem AT qizhifu effdsvmarobustmultitypebraintumorclassificationsystem AT hongrizhang effdsvmarobustmultitypebraintumorclassificationsystem AT hongweijiang effdsvmarobustmultitypebraintumorclassificationsystem |