Abstract: Deep neural networks (DNNs) have achieved significant advancements in hyperspectral image (HSI) classification, enabling critical applications in environmental monitoring, medical imaging, ...
Abstract: In the present era, Cancer-related deaths are predominantly driven by lung cancer globally, causing significant deaths across all demographics. Precise prediction and evaluation of treatment ...
Abstract: Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning ...
Abstract: The scarcity of labeled samples results in the challenge of small sample size in hyperspectral image (HSI) classification. Transfer learning offers hope for solving this problem. In ...
Abstract: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the critical importance of early and accurate detection in improving patient outcomes and treatment ...
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 ...
Abstract: 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 ...
(CNN) — Video and images showing an armed, masked individual outside the Tucson, Arizona, home of Nancy Guthrie have given new life to the search for the 84-year-old – and may offer investigators ...
Abstract: At present, mitosis detection in breast histopathology images is a critical issue for breast cancer grading. Due to the breast tissue having a complex structure, and mitosis and non-mitosis ...
Abstract: 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 ...