Patient-Specific Brain Tumor Segmentation using Hybrid Ensemble Classifier to Extract Deep Features


  • Divya Mohan Research Scholar, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
  • V. Ulagamuthalvi Professor, Computer Science, and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
  • Nisha Joseph Assistant Professor, Computer Science, Engineering, SAINTGITS College of Engineering Autonomous, Kerala, India
  • G. Kulanthaivel Professor, NITTTR, Chennai, India


tumor, texture, deep features, classifier


The abnormal cell development in the Brain is referred to as a tumor. Brain tumors are treated by physicians using radiation and Surgery. The brain tumor is categorized as benign or malignant. The benign tumor can be treated and cured using the appropriate medication suggested. A malignant tumor is an abnormal tissue that affects nearby tissues and can be cured only through proper Surgery by a physician. Manual identification of malignant and benign tumors is a time-consuming and error-prone process. An automatic brain tumor classification technique is proposed to overcome the limitation. An efficient methodology for the detection of brain tumors is done. Initially, the brain MRI image is smoothed and enhanced by a Gaussian filter. Then deep and texture features are extracted. The proposed work uses an ensemble technique using three different classifiers based on Majority Voting Method. The specified method is tested on BRATS 2017 and 2018 datasets. The results are compared with recent methods and prove efficacy.


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Proposed System Architecture




How to Cite

Mohan, D. ., Ulagamuthalvi, V. ., Joseph, N. ., & Kulanthaivel, G. . (2023). Patient-Specific Brain Tumor Segmentation using Hybrid Ensemble Classifier to Extract Deep Features. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 127–135. Retrieved from



Research Article