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In recent years, research on infrared and visible image fusion has mainly focused on deep learning‐based approaches, particularly deep neural networks with auto‐encoder architectures. However, these approaches suffer from problems such as insufficient feature extraction capability and inefficient fusion strategies. Therefore, this paper introduces a novel image fusion network to address the limitations of infrared and visible image fusion networks with auto‐encoder architectures. In the designed network, the encoder employs a multi‐branch cascade structure, and these convolution branches with different kernel sizes provide the encoder with an adaptive receptive field to extract multi‐scale features. In addition, the fusion layer incorporates a non‐local attention module that is inspired by the self‐attention mechanism. With its global receptive field, this module is used to build a non‐local attention fusion network, which works together with the l1${l}_1$‐norm spatial fusion strategy to extract, split, filter, and fuse global and local features. Comparative experiments on the TNO and MSRS datasets demonstrate that the proposed method outperforms other state‐of‐the‐art fusion approaches. [ABSTRACT FROM AUTHOR]

The automatic segmentation of brain tumours is a critical task in patient disease management. It can help specialists easily identify the location, size, and type of tumour to make the best decisions regarding the patients' treatment process. Recently, deep learning methods with attention mechanism helped increase the performance of segmentation models. The proposed method consists of two main parts: the first part leverages a deep neural network architecture for biggest tumour detection (BTD) and in the second part, ResNet152V2 makes it possible to segment the image with the attention block and the extraction of local and global features. The custom attention block is used to consider the most important parts in the slices, emphasizing on related information for segmentation. The results show that the proposed method achieves average Dice scores of 0.81, 0.87 and 0.91 for enhancing core, tumour core and whole tumour on BraTS2020 dataset, respectively. Compared with other segmentation approaches, this method achieves better performance on tumour core and whole tumour. Further comparisons on BraTS2018 and BraTS2017 validation datasets show that this method outperforms other models based on Dice score and Hausdorff criterion. [ABSTRACT FROM AUTHOR]

The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet. [ABSTRACT FROM AUTHOR]