Original scientific paper
https://doi.org/10.32985/ijeces.15.2.5
Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach
Sugandha Singh
; Department of Computer Science Babasaheb Bhimrao Ambedkar University Vidya Vihar, Rae Bareli Road, Lucknow (U.P.) 226025, INDIA
*
Vipin Saxena
; Department of Computer Science Babasaheb Bhimrao Ambedkar University Vidya Vihar, Rae Bareli Road, Lucknow (U.P.) 226025, INDIA
* Corresponding author.
Abstract
Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.
Keywords
tumor images; graph-based approach; threshold segmentation; CNN; tumor identification; meningioma;
Hrčak ID:
314574
URI
Publication date:
23.2.2024.
Visits: 745 *