Abstract: A novel unsupervised algorithm, named deep-learning-based error image prior (DLEIP), is proposed for lung electrical impedance tomography (EIT). An ...

Abstract: Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional ...

Abstract: We present a novel and robust deep-learning architecture that takes into account the pathological characteristics of eye diseases on color fundus images. The proposed hybrid architecture is ...

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...

Abstract: Deep learning models have achieved impressive results across various image processing and computer vision tasks. However, they often require large datasets, lack transparency, and struggle ...

Abstract: Road segmentation is a key task in remote sensing semantic segmentation, and the existing deep learning methods still have the problems of insufficient fineness, difficulty in modeling ...

Abstract: To tackle the challenge of data diversity in sentiment analysis and improve the accuracy and generalization ability of sentiment analysis, this study first cleans, denoises, and standardizes ...