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

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

EICSeg: Universal Medical Image Segmentation via Explicit In-Context Learning

Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...
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

Efficient Satellite Image Retrieval Using Shearlet Transforms and Spiking Neurons-Based Liquid Autoencoder Model

Abstract: This paper introduces an innovative content-based image retrieval system for precise and effective retrieval of satellite images. The system integrates liquid autoencoders with shearlet ...
IEEE

Crispvision: Apple Leaf Disease Detection Using Machine Learning and Deeper CNN

Abstract: Identifying diseases in apple leaves plays a vital role in boosting farm productivity and preventing crop losses. This research introduces a comprehensive approach for classifying images of ...
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, ...
IEEE

Enhancing Federated Learning Efficiency for Mask Detection: A Case Study on Non-IID Data and Training Configurations

Abstract: This study investigates the effects of varying federated learning (FL) arrangements and non-independent and identically distributed (Non-IID) data partitions on model performance. The ...