Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India.
Department of Electronics and Communication, Koneru Lakshmaiah Education Foundation (Deemed to Be University), Andhra Pradesh, Vaddeswaram, India.
Comput Intell Neurosci. 2022 Aug 24;2022:7797094. doi: 10.1155/2022/7797094. eCollection 2022.
The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.
脑细胞的异常和过度生长可能导致脑瘤的形成。脑瘤是癌症死亡的主要原因。因此，越来越难以找到对特定类型的脑瘤有效的治疗方法。可以使用标准的 MRI 扫描对大脑进行三维成像。其主要功能是检查、识别、诊断和分类各种神经疾病。放射疗法用于治疗肿瘤，MRI 分割用于指导治疗。因此，我们能够评估 MRI 发现的部位是否为肿瘤。本研究使用 MRI 扫描提出了一种机器学习和医学辅助的多模态方法来分割和分类脑肿瘤。MRI 图像包含噪声。在图像预处理过程中使用几何均值滤波器来去除噪声。模糊 c-均值算法负责将图像分割成更小的部分。通过分割来帮助识别感兴趣的区域。使用 GLCM 灰度共生矩阵来进行降维处理。GLCM 算法用于从照片中提取特征。然后使用各种机器学习方法（包括 SVM、RBF、ANN 和 AdaBoost）对照片进行分类。在脑肿瘤的分类和检测方面，SVM RBF 算法的性能更优。
