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, ...

To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image ...

Accurate classification of otoscopic ear images is crucial for early diagnosis of ear pathologies such as Chronic Otitis Media, Earwax Plug, and Myringosclerosis. In this study, we propose a novel ...

Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural ...

The work in this project helps in improving the classification of skin diseases using the combination of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). GANs were used ...

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, ...

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 ...

Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the ...

To fully exploit the physical information embedded in auroral images, this paper proposes a multi-label auroral classification method, termed MLAC, which integrates convolutional neural network (CNN) ...

In the field of disease diagnosis where only a small dataset of medical images may be accessible, the light-weight convolutional neural network (CNN) has become popular because it can help to avoid ...