Almalki Yassir Edrees, Ali Muhammad Umair, Ahmed Waqas, Kallu Karam Dad, Zafar Amad, Alduraibi Sharifa Khalid, Irfan Muhammad, Basha Mohammad Abd Alkhalik, Alshamrani Hassan A, Alduraibi Alaa Khalid
Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia.
Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea.
Life (Basel). 2022 Jul 20;12(7):1084. doi: 10.3390/life12071084.
Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient's life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.
由于无法治愈，脑肿瘤会缩短预期寿命。此外，其诊断涉及复杂且昂贵的程序，如磁共振成像（MRI）以及耗时且细致的检查以确定其严重程度。然而，在早期阶段及时诊断脑肿瘤可能挽救患者生命。因此，这项工作利用MRI结合机器学习方法来及时诊断脑肿瘤的严重程度（胶质瘤、脑膜瘤、无肿瘤和垂体瘤）。提取MRI高斯和非线性尺度特征是因为它们在旋转、缩放和噪声问题方面具有鲁棒性，而这些问题在诸如纹理、局部二值模式、定向梯度直方图等图像处理特征中很常见。对于这些特征，每个MRI被分解为多个8×8像素的小MR图像以捕捉小细节。为了解决内存问题，基于方差选择最强的特征并将其分割为400个高斯和400个非线性尺度特征，并且将这些特征与每个MRI进行混合。最后，利用经典机器学习分类器来检查所提出的混合特征向量的性能。使用一个可用的在线脑MRI图像数据集来验证所提出的方法。结果表明，支持向量机训练的模型具有95.33%的最高分类准确率，且计算时间较短。还将结果与近期文献进行了比较，这表明所提出的模型对临床医生早期诊断脑肿瘤可能会有帮助。
