Article: An accurate and efficient multi-task brain tumour detection with segmented MRI images using auto-metric adolescent neural network Journal: International Journal of Computational Vision and Robotics (IJCVR) 2025 Vol.15 No.4 pp.470 - 487 Abstract: Early diagnosis of a brain tumour (BT) boosts that the patient will survive after medication. Several existing methods for detecting BTs are intrusive, cumbersome, and vulnerable to human errors. This manuscript introduces a novel hybrid method, auto-metric graph adolescent identity neural network (AGAINN), for accurate and efficient human BT segmentation and multi-task detection using magnetic resonance imaging (MRI) images. The input brain MRI images are given to structural interval gradient filtering (SIGF) based preprocessing method for eliminating noise, resizing and increasing the excellence of brain images and then provided into adaptive transfer density peaks search (ATDPS) clustering based segmentation for finding the region of interest (RoI) of the preprocessed image. Then, three types of feature extraction are done using empirical wavelet transform (EWT) and grey-level co-occurrence matrix (GLCM). The extracted image features are transferred into the suggested scheme for detecting tumour and also the types of tumour also performance analyses are compared using several metrics, statistical tests and improved accuracy rate. Inderscience Publishers - linking academia, business and industry through research

Title: An accurate and efficient multi-task brain tumour detection with segmented MRI images using auto-metric adolescent neural network

Authors: Amrapali Kishanrao Salve; Kalpana C. Jondhale

Addresses: Department of Electronics and Telecommunication Engineering, Mahatma Gandhi Mission's College of Engineering, Nanded, Maharashtra-431605, India ' Department of Electronics and Telecommunication Engineering, Mahatma Gandhi Mission's College of Engineering, Nanded, Maharashtra-431605, India

Abstract: Early diagnosis of a brain tumour (BT) boosts that the patient will survive after medication. Several existing methods for detecting BTs are intrusive, cumbersome, and vulnerable to human errors. This manuscript introduces a novel hybrid method, auto-metric graph adolescent identity neural network (AGAINN), for accurate and efficient human BT segmentation and multi-task detection using magnetic resonance imaging (MRI) images. The input brain MRI images are given to structural interval gradient filtering (SIGF) based preprocessing method for eliminating noise, resizing and increasing the excellence of brain images and then provided into adaptive transfer density peaks search (ATDPS) clustering based segmentation for finding the region of interest (RoI) of the preprocessed image. Then, three types of feature extraction are done using empirical wavelet transform (EWT) and grey-level co-occurrence matrix (GLCM). The extracted image features are transferred into the suggested scheme for detecting tumour and also the types of tumour also performance analyses are compared using several metrics, statistical tests and improved accuracy rate.

Keywords: benign; malignant; brain tumour; magnetic resonance imaging; MRI; clustering; statistical analysis.

DOI: 10.1504/IJCVR.2025.147489

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.4, pp.470 - 487

Received: 17 Feb 2023
Accepted: 15 Nov 2023
Published online: 18 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: An accurate and efficient multi-task brain tumour detection with segmented MRI images using auto-metric adolescent neural network

Keep up-to-date