3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Advanced Neural Network Applications · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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