Ahmadi Mohsen, Sharifi Abbas, Jafarian Fard Mahta, Soleimani Nastaran
Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
Department of Mechanical Engineering, Urmia University of Technology, Urmia, Iran.
Int J Neurosci. 2023 Jan;133(1):55-66. doi: 10.1080/00207454.2021.1883602. Epub 2021 Feb 12.
Detection of brain tumors plays a critical role in the treatment of patients. Before any treatment, tumor segmentation is crucial to protect healthy tissues during treatment and to destroy tumor cells. Tumor segmentation involves the detection, precise identification, and separation of tumor tissues. In this paper, we provide a deep learning method for the segmentation of brain tumors. In this article, we used a convolutional neural network (CNN) to segment tumors in seven types of brain disease consisting of Glioma, Meningioma, Alzheimer's, Alzheimer's plus, Pick, Sarcoma, and Huntington. First, we used the feature-reduction-based method robust principal component analysis to find tumor location and spot in a dataset of Harvard Medical School. Then we present an architecture of the CNN method to detect brain tumors. Results are depicted based on the probability of tumor location in magnetic resonance images. Results show that the presented method provides high accuracy (96%), sensitivity (99.9%), and dice index (91%) regarding other investigations. The provided unsupervised method for tumor clustering and proposed supervised architecture can be potential methods for medical uses.
脑肿瘤的检测在患者治疗中起着关键作用。在任何治疗之前，肿瘤分割对于在治疗期间保护健康组织以及破坏肿瘤细胞至关重要。肿瘤分割涉及肿瘤组织的检测、精确识别和分离。在本文中，我们提供了一种用于脑肿瘤分割的深度学习方法。在本文中，我们使用卷积神经网络（CNN）对由胶质瘤、脑膜瘤、阿尔茨海默病、阿尔茨海默病合并症、匹克病、肉瘤和亨廷顿病组成的七种脑部疾病中的肿瘤进行分割。首先，我们使用基于特征约简的稳健主成分分析方法在哈佛医学院的数据集中找到肿瘤位置和病灶。然后我们提出了一种CNN方法的架构来检测脑肿瘤。结果基于磁共振图像中肿瘤位置的概率进行描述。结果表明，与其他研究相比，所提出的方法具有较高的准确率（96%）、灵敏度（99.9%）和骰子系数（91%）。所提供的用于肿瘤聚类的无监督方法和所提出的有监督架构可能是医学应用的潜在方法。
