Title: Multi-modal feature fusion and its application to brain tumour classification

Authors: Jyostna Devi Bodapati; N. Veeranjaneyulu; B. Naga Mounika; B. Suvarna

Addresses: Vignan's Foundation for Science Technology and Research, Vadlmudi, Guntur, India ' Vignan's Foundation for Science Technology and Research, Vadlmudi, Guntur, India ' Vignan's Foundation for Science Technology and Research, Vadlmudi, Guntur, India ' Vignan's Foundation for Science Technology and Research, Vadlmudi, Guntur, India

Abstract: In this work the objective is to detect whether the given MRI image of a brain tumour is malignant or benign. Recent literature shows that support vector machines (SVM) exhibit high generalisation ability even trained with small set of training data. Initially the images are preprocessed, segmented, different types of features like discrete wavelet transform (DWT) or gist features are extracted from the given MR images, followed by SVM-based classification. Motivated from the fusion-based classification models, in this work we have extracted different representations from the given MR images and fused them to represent the image as a single feature vector. We have applied different fusion techniques to improve the performance. Our experimental studies on bench mark datasets show that fusion techniques can enhance the accuracy of SVM classification for brain tumour classification. Along with fusion we analyse the efficiency of various kernels on the classifier's performance.

Keywords: support vector machine; SVM; Otsu segmentation; discrete wavelet transform; DWT; non-local mean filter; fusion.

DOI: 10.1504/IJAIP.2024.143815

International Journal of Advanced Intelligence Paradigms, 2024 Vol.29 No.4, pp.305 - 321

Received: 21 Aug 2018
Accepted: 05 Dec 2018
Published online: 08 Jan 2025 *

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