Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt.
Comput Intell Neurosci. 2022 Jun 21;2022:1830010. doi: 10.1155/2022/1830010. eCollection 2022.
Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.
脑肿瘤是成年人和儿童中常见的第 10 大死因。根据纹理、区域和形状，存在各种类型的肿瘤，每种肿瘤的存活率都很低。错误的分类可能会导致更糟糕的后果。因此，这些肿瘤必须被适当分为多个类别或级别，这就是多类分类发挥作用的地方。磁共振成像 (MRI) 图像是表示人脑以识别各种肿瘤的最可接受的方式或方法。图像分类技术的最新发展取得了重大进展，在该领域被认为最好的最受欢迎和更好的方法是 CNN，因此，在本文中，CNN 用于脑肿瘤分类问题。所提出的模型成功地能够将脑图像分为四个不同的类别，即没有肿瘤，这表明给定的脑 MRI 没有肿瘤、神经胶质瘤、脑膜瘤和垂体瘤。该模型的准确率为 99%。
