Department of Computer Science, University of Wah, Wah 47040, Pakistan.
National University of Technology (NUTECH), Islamabad, Pakistan.
Comput Intell Neurosci. 2022 Apr 14;2022:3236305. doi: 10.1155/2022/3236305. eCollection 2022.
A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.
脑肿瘤是细胞异常增生的结果，如果不能得到正确的诊断。早期发现脑肿瘤对于临床实践和生存率至关重要。脑肿瘤的形状、大小和特征各不相同，治疗方案也各不相同。手动检测肿瘤既困难又耗时，而且容易出错。因此，对于计算机化的诊断系统来说，准确地检测脑肿瘤存在着巨大的需求。在这项研究中，从 inceptionv3 模型中提取了深度特征，其中得分向量是从 softmax 中获取的，并提供给量子变分分类器（QVR），以区分胶质瘤、脑膜瘤、无肿瘤和垂体瘤。分类后的肿瘤图像被传递到所提出的 Seg-network 中，在那里对实际感染区域进行分割，以分析肿瘤的严重程度。报告的研究结果在三个基准数据集上进行了评估，如 Kaggle、2020-BRATS 和本地收集的图像。该模型的检测评分超过 90%，证明了所提出的模型的有效性。
