IEEE

MCBL-UNet: A Hybrid Mamba-CNN Boundary Enhanced Light-weight UNet for Placenta Ultrasound Image Segmentation

Abstract: The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images ...
IEEE

BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

Abstract: Accurate medical image segmentation is vital for clinical quantification, disease diagnosis, treatment planning, and other applications. Convolution-based U-shaped architectures excel at ...
IEEE

Hybrid CNN-Transformer Model for Accurate Classification of Human Attention Levels Using Workplace EEG Data

Abstract: Accurately detecting human attention levels is a key challenge in cognitive neuroscience, with broad application value in improving productivity. Although Electroencephalography (EEG) ...
IEEE

Enhanced Student Learning Through Facial Emotion Recognition Using Normalized CNN and Deep Learning

Abstract: The identification of facial emotions through FER serves as a vital factor for both human-computer relationships and learning platforms designed for individual needs. The research presents ...
IEEE

A Heterogeneous CNN with Transformers for Image Denoising

Abstract: Deep learning models often emphasize structural information over long-range dependencies when producing cleaner images. To enhance the robustness of the resulting denoisers, this work ...
IEEE

A Lightweight Spatial–Spectral Deformable CNN for UAV Hyperspectral Image Classification

Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
IEEE

BrainSMM: Lifespan Brain Segmentation Model With Metadata-Driven Prompt Learning

Abstract: Accurate and automatic segmentation of lifespan brain MRI into regions of interest (ROIs) is crucial for studying brain development, aging, and early diagnosis of neurological diseases.
IEEE

SMCL: Toward Semi-Supervised Automatic Modulation Recognition via Semantic Mask Contrastive Learning

Abstract: Automatic modulation recognition (AMR) is essential for ensuring the physical-layer security for Internet of Things (IoT) networks. Despite advancements in deep learning, most current AMR ...
IEEE

Multi-scale Hybrid CNN-GNN Network with Attention-Guided Fusion for Echocardiography Segmentation

Abstract: Medical image segmentation methods typically utilize convolutional neural network (CNN) with pooling layers to expand the receptive field for capturing high-level features. Due to the ...
IEEE

Detection of Powdery Mildew in Cherry Plant Leaves using Machine Learning and CNN

Abstract: Timed observation and accurate detection of plant diseases is vital for minimizing the damage to the crops and securing food for the long term, but particularly with cherry powdery mildew ...
IEEE

Retinal Vessel Segmentation Based on Intuitionistic Fuzzy Support Vector Machine

Abstract: The morphological characteristics of retinal blood vessels play an essential role in the computer-assisted diagnosis of fundus-related diseases. In this paper, a retinal vessel segmentation ...
IEEE

Machine Learning-based Customer Segmentation with Optimal Cluster Selection

In Algorithms for Machine Learning Before applying modern clustering algorithms, data was analyzed using rulebased grouping, eye scanning, manual computation of distances, and hierarchical sorting, ...