论文标题
使用计算机视觉检测脑肿瘤检测的辅助诊断工具
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision
论文作者
论文摘要
如今，在美国，有70万人患有脑肿瘤。除非采取必要的预防作用，否则脑肿瘤可以很快扩散到大脑的其他部位和脊髓。因此，这种疾病的存活率均小于男性和女性的40％。对某些人的生与死之间的差异可能是对脑肿瘤的结论性和早期诊断。但是，脑肿瘤检测和分割是乏味且耗时的过程，因为它只能由放射科医生和临床专家完成。使用计算机视觉技术，例如蒙版R卷积神经网络（Mask R CNN）来检测和分段脑肿瘤，可以减轻人体错误的可能性，同时提高预测准确率。该项目的目的是为脑肿瘤检测和分割创建辅助诊断工具。转移学习与蒙版R CNN一起使用，并因此改变了必要的参数作为起点。该模型接受了20个时期的训练，后来进行了测试。预测细分与地面真相相匹配90％。这表明该模型能够高水平执行。完成模型后，创建了在烧瓶上运行的应用程序。该应用程序将成为医疗专业人员的工具。它使医生可以上传患者脑肿瘤MRI图像，以便为每个患者的诊断和分割取得立即的结果。
Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R Convolutional Neural Network (Mask R CNN), to detect and segment brain tumors can mitigate the possibility of human error while increasing prediction accuracy rates. The goal of this project is to create an assistive diagnostics tool for brain tumor detection and segmentation. Transfer learning was used with the Mask R CNN, and necessary parameters were accordingly altered, as a starting point. The model was trained with 20 epochs and later tested. The prediction segmentation matched 90% with the ground truth. This suggests that the model was able to perform at a high level. Once the model was finalized, the application running on Flask was created. The application will serve as a tool for medical professionals. It allows doctors to upload patient brain tumor MRI images in order to receive immediate results on the diagnosis and segmentation for each patient.
