Title: Enhanced feature extraction technique using multiple pre-trained convolutional neural networks for improved brain tumour detection in MRI images

Authors: Michael Chi Seng Tang

Addresses: School of Computing and Creative Media, University of Technology Sarawak, 1, Jalan University, 96000, Sibu, Sarawak, Malaysia

Abstract: Accurate brain tumour detection in MRI images remains challenging due to the tumour's variability in appearance from image to image. This paper proposes an improved technique for identifying brain tumours in the images of MRI. To begin, features are extracted from MRI images using ResNet50 and ResNet101. The Chi-square test is then used to identify the five most significant features in each network. Concatenation of the features results in ten features per image. These features are used to train a classifier called K-nearest neighbour (KNN). The trained classifier is then evaluated on images from the testing set. The proposed method demonstrated accuracy, sensitivity, specificity, and precision values of 0.8492, 0.9351, 0.7143, and 0.8372, respectively. A comparison of performance demonstrates that the proposed method outperforms previously used methods for brain tumour detection on the same dataset in terms of accuracy, sensitivity, and precision.

Keywords: brain tumour detection; feature extraction; image processing; machine learning; medical imaging; computer vision; convolutional neural network; CNN; MRI images; artificial intelligence; feature selection.

DOI: 10.1504/IJCVR.2026.155534

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.2, pp.237 - 246

Accepted: 02 Dec 2023
Published online: 05 Aug 2026 *

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