双特征交叉融合网络用于脑肿瘤精确分类:一种神经计算方法。

IF 1.5 4区 医学 Q4 NEUROSCIENCES
Muthalakshmi M, Surya G, Mininath Bendre, Mahesh Nirmal
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引用次数: 0

摘要

脑肿瘤是一种重大的神经挑战,影响所有年龄组的个体。准确、及时地诊断肿瘤类型对制定有效的治疗方案至关重要。磁共振成像(MRI)仍然是主要的诊断方式,因为它的非侵入性和提供详细的脑成像的能力。然而,传统的肿瘤分类依赖于专家解释,费时且容易主观。本研究提出了一种新的深度学习架构,即双特征交叉融合网络(DF-CFN),用于使用MRI数据自动分类脑肿瘤。该模型集成了用于捕获全局上下文特征的卷积神经网络(ConvNeXt)和用于提取局部特征的结合特征通道注意网络(FcaNet)的浅CNN。这些通过交叉特征融合机制进行融合,以改进分类。该模型使用包含四种肿瘤类别(胶质瘤、脑膜瘤、垂体和非肿瘤)的Kaggle数据集进行训练和验证,准确率达到99.33%。使用Figshare数据集进一步证实了其泛化性,准确率达到99.22%。通过与基线模型和最新模型的对比分析,验证了DF-CFN在精度和鲁棒性方面的优越性。这种方法在帮助临床医生进行可靠的脑肿瘤分类,从而提高诊断效率和减轻医疗保健专业人员的负担方面显示出强大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual-feature cross-fusion network for precise brain tumor classification: a neurocomputational approach.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types is critical for effective treatment planning. Magnetic Resonance Imaging (MRI) remains a primary diagnostic modality due to its non-invasive nature and ability to provide detailed brain imaging. However, traditional tumor classification relies on expert interpretation, which is time-consuming and prone to subjectivity. This study proposes a novel deep learning architecture, the Dual-Feature Cross-Fusion Network (DF-CFN), for the automated classification of brain tumors using MRI data. The model integrates ConvNeXt for capturing global contextual features and a shallow CNN combined with Feature Channel Attention Network (FcaNet) for extracting local features. These are fused through a cross-feature fusion mechanism for improved classification. The model is trained and validated using a Kaggle dataset encompassing four tumor classes (glioma, meningioma, pituitary and non-tumor), achieving an accuracy of 99.33%. Its generalizability is further confirmed using the FigShare dataset, yielding 99.22% accuracy. Comparative analyses with baseline and recent models validate the superiority of DF-CFN in terms of precision and robustness. This approach demonstrates strong potential for assisting clinicians in reliable brain tumor classification, thereby improving diagnostic efficiency and reducing the burden on healthcare professionals.

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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
132
审稿时长
2 months
期刊介绍: The International Journal of Neuroscience publishes original research articles, reviews, brief scientific reports, case studies, letters to the editor and book reviews concerned with problems of the nervous system and related clinical studies, epidemiology, neuropathology, medical and surgical treatment options and outcomes, neuropsychology and other topics related to the research and care of persons with neurologic disorders.  The focus of the journal is clinical and transitional research. Topics covered include but are not limited to: ALS, ataxia, autism, brain tumors, child neurology, demyelinating diseases, epilepsy, genetics, headache, lysosomal storage disease, mitochondrial dysfunction, movement disorders, multiple sclerosis, myopathy, neurodegenerative diseases, neuromuscular disorders, neuropharmacology, neuropsychiatry, neuropsychology, pain, sleep disorders, stroke, and other areas related to the neurosciences.
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