Department of Biomedical Engineering, Hebei University of Technology, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin 300130, China.
Curr Med Imaging. 2021;17(10):1248-1255. doi: 10.2174/1573405617666210224113315.
Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy.
The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015.
The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values.
The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models.
Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.
医学图像处理是一个令人兴奋的研究领域。在本文中，我们提出了一种新的基于磁共振脑图像的脑肿瘤检测和分类模型，旨在帮助医生实现脑肿瘤的早期检测和分类，具有高性能和最佳准确性。
该模型使用包括 BRATS2012、BRATS2013、BRATS2014、BRATS2015 和 ISLES-SISS 2015 在内的五个数据库进行训练和验证。
所提出的混合模型的优势在于其新颖性，这是首次应用；我们的新方法基于混合深度卷积神经网络和深度分水岭自动编码器（CNN-DWA）模型。该方法包括六个阶段，第一阶段是输入磁共振图像，第二阶段是使用滤波器和形态学操作进行预处理，第三阶段是表示磁共振脑图像的矩阵，第四阶段是应用混合 CNN-DWA，第五阶段是脑肿瘤分类和检测，第六阶段是使用五个值评估模型的性能。
我们的混合 CNN-DWA 模型的新颖性表现出最佳的结果和高性能，准确率约为 98%，验证损失为 0、1。混合模型可以使用磁共振图像清晰地分类和检测肿瘤；与 CNN、DNN 和 DWA 等其他模型相比，我们发现所提出的模型性能更好。
基于所提出的混合模型的更好性能，这有助于开发用于早期检测脑肿瘤的计算机辅助系统，并帮助医生更好地诊断患者。
