IEEE

3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

Abstract: The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed ...
IEEE

Generative Images of Microscopy Cell Arrangements Using AI Conditional Training

Abstract: Advances in artificial intelligence-driven techniques are poised to revolutionize our understanding of cellular biology. Synthetic imaging capabilities elevate the precision and efficiency ...
IEEE

Brain Cancer MRI Classification using CNN Architectures with Grid Search Enhanced with Coordinate Ascent Optimization

Abstract: Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively ...
IEEE

CCIGeo: Cross-View and Cross-Day-Night Image Geo-Localization Using Daytime Image Supervision

Abstract: Cross-view image geo-localization is a technique to determine the geographic location of the query image by matching it with geo-tagged aerial images. However, when the query image is ...
IEEE

Visual Malware Classification Using a CNN

Abstract: Malware classification m ethods a re o ften costly, requiring constant retraining and large amounts of computing power in order to support large models that analyze software in numerous ways ...
IEEE

HZSCM: Hyperspectral Image Zero-Shot Classification via Vision-Language Models

Abstract: Most hyperspectral image (HSI) classification methods assume that all classes in the test set are present during training. However, in real-world applications, acquiring labeled training ...
IEEE

Robust X-ray Image Classification using Vision Transformers: A Comparative Study with Convolutional Neural Networks

Abstract: Accurate classification of X-ray images is crucial for detecting COVID-19 and pneumonia cases. This study compares the performance of Vision Transformers (ViTs) and Convolutional Neural ...
IEEE

Cross-Domain Hyperspectral Image Classification via Mamba–CNN and Knowledge Distillation

Abstract: Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on ...
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

DBMLLA: Double-Branch Mamba-Like Linear Attention Network for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) and transformers have made remarkable achievements in hyperspectral image classification (HSIC). Unfortunately, CNN-based methods struggle to capture the ...
IEEE

Multilevel Multimodal CNN–Transformer Fusion Network for Land Use and Land Cover Classification

Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
IEEE

SPGFormer: Structure Perception Graph Transformer With Laplacian Position Encoding for Hyperspectral Image Classification

Abstract: With the integration of graph structure representation and self-attention mechanism, the graph Transformer (GT) demonstrates remarkable effectiveness in hyperspectral image (HSI) ...