Weather image classification is a critical component of the vision systems in autonomous driving systems (ADSs), facilitating accurate decision-making across diverse driving conditions. Adverse ...

High-resolution remote sensing image (HRSI) scene classification often faces challenges; for example, the intraclass similarity is low, but the interclass similarity is high due to complex backgrounds ...

In recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures ...

In recent years, vision transformer (ViT) has achieved remarkable breakthroughs in fine-grained visual classification (FGVC) because of its self-attention mechanism that excels in extracting ...

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, ...

Auroral image classification has long been a focus of research in auroral physics. However, current methods for automatic auroral classification typically assume that only one type of aurora is ...

Object-oriented convolutional neural network (CNN) has been proven to be an effective classification method for very fine spatial resolution remotely sensed imagery. It can obtain higher accuracy and ...

Histopathological image classification stands as a cornerstone in the pathological diagnosis workflow, yet it remains challenging due to the inherent complexity of histopathological images. Recently, ...

The growth of vision transformer (ViT) methods have been quite enormous since its features provide efficient outcome in image classification, and identification. Inspired of this beneficial, this ...

Every part of the world carries skin diseases which span from minor to potentially fatal conditions including skin cancer. Accurate early diagnosis of diseases depends on a time-intensive manual ...

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 tomography images ...

Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies quantitative ...