Abd El Kader Isselmou, Xu Guizhi, Shuai Zhang, Saminu Sani, Javaid Imran, Salim Ahmad Isah
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
Brain Sci. 2021 Mar 10;11(3):352. doi: 10.3390/brainsci11030352.
The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.
脑肿瘤的分类是医学图像分析领域一项艰巨的任务。改进算法和机器学习技术有助于放射科医生在无需手术干预的情况下轻松诊断肿瘤。近年来，深度学习技术在医学图像处理和分析领域取得了卓越进展。然而，使用磁共振成像对脑肿瘤进行分类存在诸多困难：其一，脑结构复杂且其中组织相互交织；其二，由于大脑的高密度特性，对脑肿瘤进行分类存在困难。我们提出一种差分深度卷积神经网络模型（差分深度-CNN）来对不同类型的脑肿瘤进行分类，包括异常和正常的磁共振（MR）图像。在差分深度-CNN架构中使用差分算子，我们在原始CNN特征图中导出了额外的差分特征图。根据所使用的评估参数结果，该推导过程使所提方法的性能得到了提升。差分深度-CNN模型的优势在于通过对比度计算分析图像的像素方向模式，并且具有高精度对大量图像数据库进行分类且不存在技术问题的高能力。因此，所提方法具有出色的整体性能。为了测试和训练该模型的性能，我们使用了一个由25000张脑磁共振成像（MRI）图像组成的数据集，其中包括异常和正常图像。实验结果表明，所提模型的准确率达到了99.25%。这项研究表明，所提差分深度-CNN模型可用于促进脑肿瘤的自动分类。
