Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia.
College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
Curr Med Imaging. 2021;17(1):56-63. doi: 10.2174/1573405616666200311122429.
Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex.
Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy.
In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method.
The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured.
The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.
脑肿瘤的检测是一项复杂的任务，需要专业的技能和解释技术。准确地从磁共振图像中分类和分割脑肿瘤为医疗提供了重要的选择。磁共振图像中的不同对象具有相似的大小、形状和密度，这使得肿瘤的分类和分割更加复杂。
使用深度特征和不同的分类器对脑磁共振图像进行分类，以实现更高的准确性。
本研究提出了一种新的四步流程；图像增强和压缩的预处理、使用卷积神经网络（CNN）进行特征提取、使用多层感知机进行分类，最后使用增强的模糊 c 均值方法进行肿瘤分割。
该系统在四个模态中的 65 个病例上进行了测试，包括来自 BRATS-2015 数据集的 40300 个磁共振图像。这些图像包括 26 个低级别胶质瘤（LGG）肿瘤病例和 39 个高级别胶质瘤（HGG）肿瘤病例。基于 CNN 特征的分类技术优于现有方法，平均准确率达到 98.77%，并且分割结果有明显的提高。
提出的脑磁共振图像分类方法可以用于检测Glioma 肿瘤，因为它可以得到更高的准确性。
