Background: The collection of anomalous cells within or around the brain is stated as a brain tumour. Automatic brain tumour segmentation is considered a challenging task due to complexity and gradient diffusion. To improve the segmentation of 3D brain MRI images Deep Neural Network (DNN) is evolved. However, it is subjected to the drawback of training computational power and complexity. Objective: In this paper, proposed a 3D Dense AlexNet model with backpropagation for segmentation of tumour in brain MRI images. The developed architecture consists of Neural Network for processing input 3D images. This paper focused on improving the overall segmentation process with the Alexnet model for 3D brain images for performance improvement. Method: Based on the training and validation test self-constrained 3D Dense ALexNet model is developed. Within the 3D Dense AlexNet backpropagation is adopted for removing complexity in the testing process and accuracy improvement. Based on the training and testing process 3D MRI image sequences are trained and processed for segmentation on the tumour. Result: The analysis of results expressed that the proposed 3D Dense AlexNet exhibits improved segmentation performance. Based on the proposed 3D AlexNet architecture MRI images are segmented with minimal time. The performance of the proposed 3D Dense AlexNet model exhibited the improved accuracy of tumour detection with reduced computational complexity