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Brain tumors are one of the most common diseases threatening human health. Early detection and precise segmentation are of great significance for clinical diagnosis and treatment. This paper presents a Learnable Wavelet Transform and Attention Mechanism network(LWTA-Net2D), based on 2D Convolutional Neural Networks (CNN), integrating Learnable Discrete Wavelet Transform (LDWT), combination of Monte Carlo Attention (MCattn) and Monte Carlo Bottleneck Layer (MCBottleneck), and a U-Net-based encoder-decoder architecture.

The Colour-Rendered Bosphorus Projections (CRBP) Face Dataset represents an innovative advancement in facial recognition and computer vision technologies. This dataset uniquely combines the precision of 3D face modelling with the detailed visual cues of 2D imagery, creating a multifaceted resource for various research activities. Derived from the acclaimed Bosphorus 3D Face Database, the CRBP dataset introduces colour-rendered projections to enrich the original dataset.

The two distribution systems provided in this dataset are based on the data provided in [1]. The dataset was used in the analysis made in [2], where it was developed a model that can operate the distribution system considering wildfire-prone climate conditions. In this work, we consider that part of the grid is vulnerable to the ignition of a wildfire, which can be influenced by the levels of power flows passing through the line segments within the region.