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.
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 *