<p>Accurate segmentation is crucial for the efficient diagnosis and treatment planning of brain tumors. Automating this process offers substantial advantages for clinicians by reducing the time and potential errors inherent in manual segmentation.&#xa0;This paper introduces RLU-Net-GD, a deep learning-based encoder-decoder architecture designed to address challenges such as image artifacts, poor contrast, class imbalance, and high intra-class variability. The model utilizes ResNet-50 as its backbone, incorporating a residual learning mechanism to mitigate vanishing gradient issues and enhance feature propagation in deep networks. Skip connections between the encoder and decoder pathways ensure the retention of spatial details by combining fine and coarse-grained features. A guided decoder supervises the learning process to enhance the generation of detailed feature maps, while a weighted guided loss function addresses class imbalance. Transfer learning with pre-trained weights also enables effective training with limited data, further boosting segmentation accuracy.&#xa0;Experimental results on the BraTS 2018, BraTS 2019, and BraTS 2020 datasets demonstrate that RLU-Net-GD outperforms state-of-the-art methods, significantly enhancing brain tumor segmentation accuracy.&#xa0;RLU-Net-GD utilizes residual connections in the encoder to enhance feature propagation and mitigate gradient vanishing, while deep supervision in the guided decoder, employing a weighted guided loss, ensures the extraction of salient features. These innovations enable the model to address challenges such as class imbalance and poor contrast, resulting in superior segmentation performance compared to state-of-the-art methods.</p>