An Ensemble Model for the Diagnosis of Brain Tumors through MRIs

• Published in: Diagnostics (Basel) 2023-02, Vol.13 (3), p.561
• Main Authors: Ghafourian, Ehsan, Samadifam, Farshad, Fadavian, Heidar, Jerfi Canatalay, Peren, Tajally, AmirReza, Channumsin, Sittiporn
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Brain cancer; Brain research; Brain tumors; Classification; Deep learning; Diagnosis; ensemble classifier; Machine learning; Magnetic resonance imaging; Neural networks; Optimization; Performance evaluation; singular value decomposition; social spider optimization; Support vector machines; Tumors; Wavelet transforms
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13030561

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.