Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.

由于肿瘤的鲜明特征，脑肿瘤三级分类成为当代的研究课题。现有的提议采用深度神经网络进行三类分类。然而，实现高精度仍然是大脑图像分类的无尽挑战。我们提出了一种用于三类脑肿瘤分类的深度密集初始残差网络。我们使用深度密集网络和 softmax 层定制了 Inception ResNet v2 的输出层。深度密集网络提高了所提出模型的分类精度。已使用具有 3064 张图像的公开可用脑肿瘤图像数据集上的关键性能指标对所提出的模型进行了评估。我们提出的模型优于现有模型，平均准确率为 99.69%。更远，