Segmentation of white blood cells is critical for a wide range of applications. It aims to identify and isolate individual white blood cells from medical images, enabling accurate diagnosis and monitoring of diseases. In the last decade, many researchers have focused on this task using U-Net, one of the most used deep learning architectures. To further enhance segmentation accuracy and robustness, recent advances have explored the combination of U-Net with other techniques, such as attention mechanisms and aggregation techniques. However, a common challenge in white blood cell image segmentation is the similarity between the cells’ cytoplasm and other surrounding blood components, which often leads to inaccurate or incomplete segmentation due to difficulties in distinguishing low-contrast or subtle boundaries, leaving a significant gap for improvement. In this paper, we propose GAAD-U-Net, a novel architecture that integrates attention-augmented convolutions to better capture ambiguous boundaries and complex structures such as overlapping cells and low-contrast regions, followed by a gating mechanism to further suppress irrelevant feature information. These two key components are integrated in the Double U-Net base architecture. Our model achieves state-of-the-art performance on white blood cell benchmark datasets, with a 3.4% Dice score coefficient (DSC) improvement specifically on the SegPC-2021 dataset. The proposed model achieves superior performance as measured by mean the intersection over union (IoU) and DSC, with notably strong segmentation performance even for difficult images.

Emotion recognition from speech is essential for human–computer interaction (HCI) and affective computing, with applications in virtual assistants, healthcare, and education. Although deep learning has made significant advancements in Automatic Speech Emotion Recognition (ASER), the challenge still exists in the task given variation in speakers, subtle emotional expressions, and environmental noise. Practical deployment in this context depends on a strong, fast, scalable recognition system. This work introduces a new framework combining DenseNet121, especially fine-tuned for the crowd-sourced emotional multimodal actors dataset (CREMA-D), with the convolutional block attention module (CBAM). While DenseNet121’s effective feature propagation captures rich, hierarchical patterns in the speech data, CBAM improves the focus of the model on emotionally significant elements by applying both spatial and channel-wise attention. Furthermore, enhancing the input spectrograms and strengthening resistance against environmental noise is an advanced preprocessing pipeline including log-Mel spectrogram transformation and normalization. The proposed model demonstrates superior performance. To make sure the evaluation is strong even if there is a class imbalance, we point out important metrics like an Unweighted Average Recall (UAR) of 71.01% and an F1 score of 71.25%. The model also gets a test accuracy of 71.26% and a precision of 71.30%. These results establish the model as a promising solution for real-world speech emotion detection, highlighting its strong generalization capabilities, computational efficiency, and focus on emotion-specific features compared to recent work. The improvements demonstrate practical flexibility, enabling the integration of established image recognition techniques and allowing for substantial adaptability in various application contexts.