Dual phase hierarchical brain tumour detection and segmentation using unet based skip guidance residual convolutional transformer

•To automatically classify and segment the BT in MRI image, an efficient Unet based transformer model called DCARCTUnet is introduced.•To avoid the occurrence of noise and blurring in input images, the proposed method utilizes pre-processing techniques to accurately segment and classify BT.•A classification model named as EfficientNet is introduced to detect and classify BT. This model will decrease the number of parameters and boost computational efficiency.•To obtain detailed and high features, the dual phase channel attention module is introduced in the decoder block.•To generate complete global dependencies and to maintain the network’s capacity for extracting features, the Convolutional Transformer is introduced.•To replace the structure of actual skip-connection, pyramid skip guidance module (PSM) is developed. This model merge the feature maps of all higher-level stages in order to segment BT. Brain tumour segmentation and classification is an important approach to understanding tumour characteristics effectively. Accurate lesion segmentation requires many image modalities with varied contrasts. As a result, manual segmentation may not be suitable for large-scale research. The research has shown interest in developing various learning algorithms to detect and segment brain tumour (BT) images accurately based on the set of experimental images. Some of the issues noticed in the existing models, such as high computational complexity, high processing time, and less accuracy, were identified. In order to overcome these existing issues, a novel Dual-phase channel attentional Residual convolutional transformer Unet model (DCARCTUnet) is introduced for segmenting brain tumours. Initially, the input images are passed into the pre-processing stage to remove noise and blur. The Efficient Net model identified the pre-processed image as either normal or tumour. The proposed segmentation model can be integrated into three stages, namely dual phase channel attention module (DCAM), residual convolutional transformer Unet model and Pyramid skip guidance module (PSM). The proposed method uses the BRATS 2020–2021 dataset to analyze the results accurately. The results showed that the proposed model has a dice coefficient value of 0.99 and a Jaccard coefficient value of 0.98. Furthermore, performance analysis is carried out to demonstrate the effectiveness of the proposed model.