In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and human–computer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes.

Medical image analysis plays a powerful role in clinical assistance for the diagnosis and treatment of diseases. Image segmentation is an essential part of the medical imaging process as it extracts the region of interest through semi-automated or automated methods. Deep learning approaches have emerged as a fast-growing research field in medical image analysis. Vision transformers (ViT) are deep learning models that came up as a competing substitute for convolutional neural networks. ViT reports breakthroughs in computer vision tasks including object classification, detection, localization, and segmentation. Colon polyp detection and segmentation is a challenging task in the medical diagnosis and prognosis of colorectal cancer. Early detection and segmentation of polyp regions are of the utmost importance in preventing disease in later stages. In this work, we explore a hierarchical vision transformer as the backbone, replacing convolutional neural networks (CNNs) for the segmentation of polyps. The hierarchical vision transformer is composed of several stages, each having a different resolution. Through the use of a convolutional decoder, the patches from various stages are successively combined to produce full pre-dictions. The transformer backbone has a global receptive field at every stage that provide finer-grained and globally relevant predictions. Experimental results indicate that we can fine-tune the architecture to generate promising results on segmentation metrics even on smaller datasets, with mean Dice and mean IoU scores of 74% and 73% on the Kvasir-SEG dataset.