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An Efficient Brain Tumor Detection Using Mr Images With Kernel Based Pca and Svm

Year: 2025

Type: conference-paper

Abstract: The primary method for detecting brain tumors is through the use of MRI. In this paper, a new hybrid technique for classifying human brain images into benign and malignant tumors. The technique consists of several stages: categorization, feature extraction with feature reduction, and pre-processing and segmentation. The threshold function is used during the segmentation and pre-processing phases. In respect to MRI, the second stage is acquiring characteristics using DWT. In third step, KPCA is utilized to distil MRI data to their most crucial components. Finally, the classification stage employs a classifier called Kernel Support Vector Machine (KSVM) to identify the infected area in the brain tumor. Experimental results have shown that proposed technique achieves good accuracy of 97% in distinguishing normal and abnormal tissues in brain MR images. Overall, this proposed technique is effective and powerful in tumor detection compared to existing frameworks.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2025.03.198

Authors: V. Sabitha, Jagannath Nayak, P. Ramana Reddy

Institutions: Jawaharlal Nehru Technological University Anantapur, Defence Research and Development Organisation, Ministry of Defence

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4410775693

FWCI:

Cites: 28

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Related to: 10