Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation

Developing a convolutional neural network (CNN) for medical image segmentation is a complex task, especially when dealing with the limited number of available labelled medical images and computational resources. This task can be even more difficult if the aim is to develop a deep network and using a complicated structure like attention blocks. Because of various types of noises, artefacts and diversity in medical images, using complicated network structures like attention mechanism to improve the accuracy of segmentation is inevitable. Therefore, it is necessary to develop techniques to address the above difficulties. Neuroevolution is the combination of evolutionary computation and neural networks to establish a network automatically. However, Neuroevolution is computationally expensive, specifically to create 3D networks. In this paper, an automatic, efficient, accurate, and robust technique is introduced to develop deep attention convolutional neural networks utilising Neuroevolution for both 2D and 3D medical image segmentation. The proposed evolutionary technique can find a very good combination of six attention modules to recover spatial information from downsampling section and transfer them to the upsampling section of a U-Net-based network—six different CT and MRI datasets are employed to evaluate the proposed model for both 2D and 3D image segmentation. The obtained results are compared to state-of-the-art manual and automatic models, while our proposed model outperformed all of them.