Title: DDVM: dual decision voting mechanism for brain tumour identification with LBP²Q-SVM type classifier

Authors: Mansi Lather; Parvinder Singh

Addresses: Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat 131039, Haryana, India ' Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat 131039, Haryana, India

Abstract: Brain tumour classification plays a significant role in medical science as diagnosis of a brain tumour at its early stage of development can improve the recovery of the patient after treatment. In this paper, effective brain tumour presence and type classification methods are proposed. A pre-processing phase of the proposed model is capable to handle the dull medical images by contrast enhancement and noise filtering. In the first phase, to detect the tumour a dual decision voting mechanism (DDVM) for convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) classification models is proposed. The final tumour identification is done by score maximisation. In the second phase, to identify the type of tumour as high-grade glioma or low-grade glioma, a novel algorithm named LBP²Q featured support vector machine classification model is designed. The results of both phases demonstrated that the proposed scheme outperforms the existing techniques in terms of various performance matrices.

Keywords: biomedical image processing; brain tumour detection; classification model; machine learning; medical image analysis.

DOI: 10.1504/IJCVR.2023.127304

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.1, pp.52 - 72

Received: 06 Sep 2021
Accepted: 12 Dec 2021
Published online: 30 Nov 2022 *