Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey.
Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey.
Med Biol Eng Comput. 2021 Jan;59(1):57-70. doi: 10.1007/s11517-020-02290-x. Epub 2020 Nov 21.
Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.
脑癌是一种由大脑中正常细胞外异常生长的侵袭性细胞引起的疾病。随着技术机会的发展，脑癌病例的症状和诊断每天都在产生更准确的结果。在这项研究中，使用了一种名为 BrainMRNet 的深度学习模型，该模型是为开源脑磁共振图像中的大规模检测而开发的。BrainMRNet 模型包括三个处理步骤：注意力模块、超柱技术和残差块。为了证明所提出模型的准确性，本研究检查了导致脑癌的三种类型的肿瘤数据：神经胶质瘤、脑膜瘤和垂体瘤。此外，还提出了一种分割方法，该方法还确定了导致脑癌的两类肿瘤在大脑的哪个叶区更为集中。在研究中进行了分类准确率；神经胶质瘤肿瘤为 98.18%，脑膜瘤肿瘤为 96.73%，垂体瘤为 98.18%。在实验结束时，使用神经胶质瘤和脑膜瘤肿瘤图像的子集，确定了肿瘤区域在哪个脑叶中可见，并在该分析中实现了 100%的成功率。在这项研究中，提出了一种混合深度学习模型来确定脑肿瘤的检测。此外，还提出了开源软件，该软件从统计学上确定人脑的哪个叶区发生了脑肿瘤。在实验中应用和测试的方法具有很高的准确性、精度和特异性，显示出有希望的结果。这些结果表明，所提出的方法在临床环境中可用于支持关于脑肿瘤检测的医学决策。
