Title: Deep convolutional neuron attention forward harmonic network for brain tumour detection and classification using MRI image

Authors: Kalyani Ashok Bedekar; Anupama Sanjay Awati

Addresses: Visvesvaraya Technological University (VTU), Belagavi, Karnataka, 590018, India ' Department of Electronics and Communication, KLS Gogte Institute of Technology, Belagavi, Karnataka, 590018, India; Affiliated to: Visvesvaraya Technological University (VTU), India

Abstract: In recent years, brain tumours have been the deadliest brain disorder that occurs because of the collection or mass of aberrant brain tissues in the human brain. This research proposes a novel deep learning (DL) model, deep convolutional neuron attention forward harmonic network (DCNasFH-Net), for accurate detection and classification of brain tumours. At first, the input magnetic resonance imaging (MRI) image is pre-processed by utilising a high boost filter, and the attention gate ResU-Net (AGResU-Net) with a hybrid loss function is used to segment the interested brain tumour region. Following this, the features are extracted using the spatial grey-level dependence matrix (SGLDM) and statistical features. Finally, the brain tumour is detected and classified effectively by utilising DCNasFH-Net. Moreover, the DCNasFH-Net attained effectual experimental outcomes with an accuracy of 93.01%, a true negative rate (TNR) of 91.89%, and a true positive rate (TPR) of 94%.

Keywords: attention gate ResU-Net; AGResU-Net; deep convolutional neuron attention forward harmonic network; DCNasFH-Net; deep convolutional neural network; neural architecture search network; high boost filter.

DOI: 10.1504/IJAHUC.2025.149584

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.50 No.3, pp.137 - 153

Received: 18 Oct 2024
Accepted: 17 Jan 2025
Published online: 07 Nov 2025 *

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