Detection and Classification of Different Stages of Benign and Malignant Tumor of MRI Brain Using Machine Learning

Srisabarimani, K.; Arthi, R.; Parihar, Nikita; Priya, K.; Nair, S. Aman

doi:10.1007/978-981-15-7504-4_31

Cited by 4 publications

(1 citation statement)

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“…Srisabarimani et al discussed about different segmentation and classification for MRI Brain tumor images at various stages using different Machine Learning concepts [22]. Vijay Wasule and Sonar classified brain tumor using 251 images of clinical database [23].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fusion Approach to Detect Brain Tumor Using Machine Learning for MRI Images

Kaliannan¹,

Rengaraj²,

Daniel³

2022

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Medical Imaging challenges the recent researchers with variability of potential structures, positions and appearance strengths of various tumors present among the patients. The proposed work presents an effective brain tumor watershed segmentation technique created on 2D image followed by statistical feature extraction. Machine Learning models such as SVM, KNN, and XG boost were used to inspire the network design in order to extract tumor existence. The proposed segmentation algorithm has been tested and evaluated on original images that consist of an aggregate of 52 normal MRI volumes of distinctive patients with the presence of tumors or not signifying distinctive structures that obtains outcomes near to physical segmentation implementations. The novelty present in the proposed work classifies whether the tumor is present or not with an accuracy of approximately 98%.

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Section: Literature Reviewmentioning

confidence: 99%