Open Access

ARTICLE

3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

by Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*

1 Medical Imaging and Diagnostics Lab, NCAI, Department of Computer Science, COMSATS University Islamabad, Islamabad, 44000, Pakistan
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah, Pakistan
3 Department of Computer Science, HITEC University, Taxila, Pakistan
4 Department of Management Information Systems,  College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
5 Department of Computer Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Jae-Hyuk Cha. Email:

(This article belongs to the Special Issue: Cancer Diagnosis using Deep Learning, Federated Learning, and Features Optimization Techniques)

Computers, Materials & Continua 2023, 76(3), 2861-2877. https://doi.org/10.32604/cmc.2023.039181

Received 13 January 2023; Accepted 10 April 2023; Issue published 08 October 2023

Abstract

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced with the 3D Kronecker convolution, while local feature learning was performed using the 3D Feature Selection (3DFSC). A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions, yielding efficient segmentation of brain tumors of different sizes. A 3D connected component analysis with a global threshold was used as a post-processing technique. The standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model validation. Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90, 0.80, and 0.84 for the whole tumor, enhancing tumor, and tumor core, respectively. Overall, the proposed model was efficient in brain tumor segmentation, which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

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