The timely detection and accurate localization of brain tumors is crucial in preserving people’s quality of life. Thankfully, intelligent computational systems have proven invaluable in addressing these challenges. In particular, the UNET model can extract essential pixel-level features to automatically identify the tumor’s location. However, known deep learning-based works usually directly feed the 3D volume into the model, which causes excessive computational complexity. This paper presents an approach to boost the UNET network, reducing computational workload while maintaining superior efficiency in locating brain tumors. This concept could benefit portable or embedded recognition systems with limited resources for operating in real time. This enhancement involves an automatic slice selection from the MRI T2 modality volumetric images containing the most relevant tumor information and implementing an adaptive learning rate to avoid local minima. Compared with the original model (7.7 M parameters), the proposed UNET model uses only 2 M parameters and was tested on the BraTS 2017, 2020, and 2021 datasets. Notably, the BraTS2021 dataset provided outstanding binary metric results: 0.7807 for the Intersection Over the Union (IoU), 0.860 for the Dice Similarity Coefficient (DSC), 0.656 for the Sensitivity, and 0.9964 for the Specificity compared to vanilla UNET.

U-shaped architectures have achieved exceptional performance in medical image segmentation. Their aim is to extract features by two symmetrical paths: an encoder and a decoder. We propose a lightweight U-Net incorporating reverse attention and a preprocessing framework for accurate retinal vessel segmentation. This concept could be of benefit to portable or embedded recognition systems with limited resources for real-time operation. Compared to the baseline model (7.7 M parameters), the proposed U-Net model has only 1.9 M parameters and was tested on the DRIVE (Digital Retinal Images for Vesselness Extraction), CHASE (Child Heart and Health Study in England), and HRF (High-Resolution Fundus) datasets for vesselness analysis. The proposed model achieved Dice coefficients and IoU scores of 0.7871 and 0.6318 on the DRIVE dataset, 0.8036 and 0.6910 on the CHASE-DB1 Retinal Vessel Reference dataset, as well as 0.6902 and 0.5270 on the HRF dataset, respectively. Notably, the integration of the reverse attention mechanism contributed to a more accurate delineation of thin and peripheral vessels, which are often undetected by conventional models. The model comprised 1.94 million parameters and 12.21 GFLOPs. Furthermore, during inference, the model achieved a frame rate average of 208 FPS and a latency of 4.81 ms. These findings support the applicability of the proposed model in real-world clinical and mobile healthcare environments where efficiency and Accuracy are essential.

Vision Transformer (ViT) models are well known for effectively capturing global contextual information through self-attention. In contrast, ConvNeXt’s hierarchical convolutional inductive bias enables the extraction of robust multi-scale features at lower computational and memory cost, making it suitable for deployment in systems with limited annotation and constrained resources. Accordingly, a multi-scale UNet architecture based on a ConvNeXt backbone is proposed for brain tumor segmentation; it is equipped with a spatial latent module and Reverse Attention (RA)-guided skip connections. This framework jointly models long-range context and delineates reliable boundaries. Magnetic resonance images drawn from the BraTS 2021, 2023, and 2024 datasets serve as case studies for evaluating brain tumor segmentation performance. The incorporated multi-scale features notably improve the segmentation of small enhancing regions and peripheral tumor boundaries, which are frequently missed by single-scale baselines. On BraTS 2021, the model achieves a Dice similarity coefficient (DSC) of 0.8956 and a mean intersection over union (IoU) of 0.8122, with a sensitivity of 0.8761, a specificity of 0.9964, and an accuracy of 0.9878. On BraTS 2023, it attains a DSC of 0.9235 and an IoU of 0.8592, with a sensitivity of 0.9037, a specificity of 0.9977, and an accuracy of 0.9904. On BraTS 2024, it yields a DSC of 0.9225 and an IoU of 0.8575, with a sensitivity of 0.8989, a specificity of 0.9979, and an accuracy of 0.9903. Overall, the segmentation results provide spatially explicit contours that support lesion-area estimation, precise boundary delineation, and slice-wise longitudinal assessment.

Low-light color image enhancement remains a challenging task for vision-based decision systems, which must simultaneously address illumination correction, noise suppression, contrast recovery, and color preservation from a single degraded observation. This study proposes ResGASP-GAN, a GAN-based low-light image enhancement framework built around a residual-output generator that integrates batch-size-independent normalization, multi-scale contextual aggregation, and channel-wise feature recalibration within a conditional adversarial setting. Group Normalization is integrated into the proposed generator to reduce dependence on batch statistics during small-batch training, while a Squeeze-and-Excitation (SE) module adaptively enables channel-wise feature recalibration and helps preserve structural and chromatic information. The proposed generator uses reflection-padded convolutions to reduce boundary artifacts, and it features a multi-scale bottleneck composed of dilated residual blocks and Atrous Spatial Pyramid Pooling to capture spatially varying illumination patterns. The model is optimized using a compound objective that combines an adversarial term with an ${\ell }_1$ reconstruction loss, balancing perceptual realism with pixel-level fidelity. Experimental evaluation employed the LOL-v1, LOL-v2-Real, and LOL-v2-Synthetic datasets using reference-based metrics: PSNR, SSIM, and LPIPS. No-reference perceptual metrics were also used, including NIQE and BRISQUE. The results indicate that the proposed method achieves competitive structural similarity and visual image quality on LOL-v2-Real, competitive reconstruction performance on LOL-v1, and good generalization on LOL-v2-Synthetic, with the second-best metrics of PSNR = 22.30 dB, SSIM = 0.9154, and LPIPS = 0.1022 among the reported methods on this last dataset. In contrast, using no-reference metrics, this study achieved very good results, with the lowest BRISQUE of 10.4540 and a competitive NIQE of 4.4396, providing high-quality visual–perceptual reconstruction. Overall, the proposed architecture provides a competitive GAN-based alternative for low-light image enhancement, combining residual connections, multi-scale contextual modeling, and channel-wise feature refinement. This architecture provides the lowest inference time over all discussed models, which is highly required in real-time outdoor applications such as robot navigation and mapping.

Accurate diagnosis and early treating of brain tumors have a positive and significant impact on a person's quality of life. For this reason, several studies are currently being conducted to improve medical image processing and the detection and accurate localization of brain tumors. This has led to the implementation of Deep Learning (DL) in medical image analysis. Thus, this study presents a lightweight U-Net to reduce computational costs in the detection of brain tumors. This detection was performed by selecting the best slice from the Magnetic Resonance Images (MRI) of several modalities (T1C, T2W, and T2F) in the BrasTS2024 dataset. The proposed model automatically selects the best slice by extracting the slice with the major tumor content from the mask. An adaptive learning-rate scheduler was applied during training to ensure stable convergence. Among the modalities, T2W produced the best quantitative results, achieving a Dice coefficient of 0.760, an IoU of 0.623, and a sensitivity of 0.842. In contrast, T2F yielded visually comparable yet slightly lower metrics. These findings indicate that reducing model complexity needs not compromise accuracy and highlight the T2W sequence as the most informative single slice for tumor delineation