Department of Computer Science and Engineering, J.N.N Institute of Engineering, Chennai, India.
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
J Med Syst. 2019 Jul 24;43(9):294. doi: 10.1007/s10916-019-1416-0.
In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient's life chances are improved by the early detection of it. The process of diagnosing the brain tumoursby the physicians is normally carried out using a manual way of segmentation. It is time consuming and a difficult one. To solve these problems, Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method. The primary aim is to present optimization based MRIs image segmentation. Small kernels allow the design in a deep architecture. It has a positive consequence with respect to overfitting provided the lesser weights are assigned to the network. Skull stripping and image enhancement algorithms are used for pre-processing. The experimental result shows the better performance while comparing with the existing methods. The compared parameters are precision, recall and accuracy. In future, different selecting schemes can be adopted to improve the accuracy.
在医学图像处理中，脑肿瘤分割起着重要的作用。为了给患者治疗，非常需要早期发现这些肿瘤。早期发现可以提高患者的生存机会。医生通常通过手动分割的方式来诊断脑肿瘤。这个过程既耗时又困难。为了解决这些问题，提出了基于 BAT 算法的增强卷积神经网络（ECNN）和损失函数优化的自动分割方法。主要目的是提出基于优化的 MRI 图像分割。小核允许在深度架构中进行设计。如果给网络分配较小的权重，就会产生过拟合的积极结果。使用颅骨剥离和图像增强算法进行预处理。实验结果表明，与现有方法相比，它具有更好的性能。比较的参数是精度、召回率和准确率。在未来，可以采用不同的选择方案来提高准确性。
