Title: SqueezeNet-deep Kronecker net-based brain tumour classification using MRI image

Authors: Srilakshmi Aluri; Sagar Imambi Shaik

Addresses: Department of Computer Science and Engineering, K.L. Educational Foundation (Deemed to be University), Vaddeswaram, Andhra Pradesh, 522302, India ' Department of Computer Science and Engineering, K.L. Educational Foundation (Deemed to be University), Vaddeswaram, Andhra Pradesh, 522302, India

Abstract: Brain tumour diagnosis is a time-consuming process that heavily depends on the expertise and experience of radiologists. Thus, a SqueezeNet-Deep Kronecker Net (Squeeze-KNet) is devised for brain tumour classification. The proposed model provides an accurate diagnosis of brain tumours earlier. Initially, the pre-processing is done using an adaptive bilateral filter and then segmentation process is done using O-SegNet. Thereafter, the feature extraction process is done to extract the significant features like spider local image feature (SLIF), pyramid histogram of oriented gradients (PHOG), local vector pattern (LVP), Weber local binary pattern (WLBP), and local Gabor XOR patterns (LGXP). Finally, the brain tumour classification is done using the proposed Squeeze-KNet, which is the combination of deep Kronecker Net (DKN) and SqueezeNet. The proposed network is evaluated using accuracy, true positive rate (TPR), true negative rate (TNR), negative predictive value (NPV) and positive predictive value (PPV) and obtained 92.6%, 91.5%, 91.2%, 90.6%, and 90.1%.

Keywords: SqueezeNet; deep Kronecker net; DKN; brain tumour; O-SegNet; adaptive bilateral filter; local vector pattern; LVP.

DOI: 10.1504/IJIIDS.2025.145460

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.2, pp.268 - 298

Received: 19 Jan 2024
Accepted: 12 Aug 2024
Published online: 01 Apr 2025 *

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