Rahman Asif, Hayat Maqsood, Iqbal Nadeem, Alarfaj Fawaz Khaled, Alkhalaf Salem, Alturise Fahad
Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Khyber-Pakhtunkhwa, Pakistan.
Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), 31982, Al-Ahsa, Saudi Arabia.
Sci Rep. 2025 Aug 11;15(1):29411. doi: 10.1038/s41598-025-14901-4.
Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivotal in precise and accurate tumor characterization owing to its high-resolution, non-invasive nature. This study investigates the synergy among multiple feature representation schemes such as local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, Convolutional Neural Networks (CNN), and Gray-Level Run Length Matrix alongside five learning algorithms namely: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), and probabilistic neural network (PNN), and CNN. Empirical findings indicate that LBP in conjunction with SVC and CNN obtained high specificity and accuracy, rendering it a promising method for MRI-based tumor diagnosis. Further to investigate the contribution of LBP, Statistical analysis chi-square and p-value tests are used to confirm the significant impact of LBP feature space for identification of brain Tumor. In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. In subsequent analysis, a large benchmark dataset is also utilized to evaluate the performance of learning algorithms in order to investigate the generalization power of the proposed model. CNN achieves the highest accuracy of 98.9%, followed by SVC at 96.7%. These results highlight CNN's effectiveness in automated, high-precision tumor diagnosis. This achievement is ascribed with MRI-based feature extraction by combining high resolution, non-invasive imaging capabilities with the powerful analytical abilities of CNN. CNN demonstrates superiority in medical imaging owing to its ability to learn intricate spatial patterns and generalize effectively. This interaction enhances the accuracy, speed, and consistency of brain tumor detection, ultimately leading to better patient outcomes and more efficient healthcare delivery. https://github.com/asifrahman557/BrainTumorDetection .
医学成像领域的最新创新显著改善了脑肿瘤的识别，超越了传统诊断方法，传统方法存在分辨率低、辐射暴露和对比度有限等问题。磁共振成像（MRI）因其高分辨率、非侵入性的特性，在精确准确的肿瘤特征描述中起着关键作用。本研究调查了多种特征表示方案之间的协同作用，如局部二值模式（LBP）、Gabor滤波器、离散小波变换、快速傅里叶变换、卷积神经网络（CNN）和灰度游程矩阵，以及五种学习算法，即：k近邻、随机森林、支持向量分类器（SVC）、概率神经网络（PNN）和CNN。实证结果表明，LBP与SVC和CNN结合可获得高特异性和准确性，使其成为基于MRI的肿瘤诊断的一种有前途的方法。为了进一步研究LBP的贡献，使用统计分析卡方检验和p值检验来确认LBP特征空间对脑肿瘤识别的显著影响。此外，使用SHAP分析来识别分类中最重要的特征。在一个小数据集中，CNN的准确率为97.8%，而SVC的准确率为98.06%。在后续分析中，还使用了一个大型基准数据集来评估学习算法的性能，以研究所提出模型的泛化能力。CNN的准确率最高，为98.9%，其次是SVC，为96.7%。这些结果突出了CNN在自动化、高精度肿瘤诊断中的有效性。这一成就归因于通过将高分辨率、非侵入性成像能力与CNN强大的分析能力相结合的基于MRI的特征提取。CNN在医学成像中表现出优越性，因为它能够学习复杂的空间模式并有效地进行泛化。这种相互作用提高了脑肿瘤检测的准确性、速度和一致性，最终带来更好的患者治疗效果和更高效的医疗服务。https://github.com/asifrahman557/BrainTumorDetection
