Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach

被引:7
作者
Natha, Sarfaraz [1 ]
Laila, Umme [2 ]
Gashim, Ibrahim Ahmed [3 ]
Mahboob, Khalid [2 ]
Saeed, Muhammad Noman [4 ]
Noaman, Khaled Mohammed [4 ]
机构
[1] Sir Syed Univ Engn & Technol, Dept Software Engn, Karachi 75300, Pakistan
[2] Inst Business Management IOBM, Comp Sci Dept, Karachi 75190, Pakistan
[3] Jazan Univ, Coll Educ, Jazan 82817, Saudi Arabia
[4] Jazan Univ, Elearning Ctr, Jazan 82817, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 05期
关键词
brain tumor 1; MRI; 2; CNN; AlexNet; ensemble model; transfer learning; data augmentation; biomedical imaging; deep learning; CONVOLUTIONAL NEURAL-NETWORKS; CLASSIFICATION;
D O I
10.3390/app14052210
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person's life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient's survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called "SETL_BMRI", which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model's effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.
引用
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页数:19
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