AIMC Topic: Deep Learning

Clear Filters Showing 4591 to 4600 of 28423 articles

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.

Scientific reports
Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with ...

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

Scientific reports
Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the ph...

BD-StableNet: a deep stable learning model with an automatic lesion area detection function for predicting malignancy in BI-RADS category 3-4A lesions.

Physics in medicine and biology
The latest developments combining deep learning technology and medical image data have attracted wide attention and provide efficient noninvasive methods for the early diagnosis of breast cancer. The success of this task often depends on a large amou...

A fact based analysis of decision trees for improving reliability in cloud computing.

PloS one
The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses...

DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas.

PloS one
The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable pote...

Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

PloS one
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning,...

Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances...

Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum.

Talanta
Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease transmission. This study proposed an AI model for precisely identifying foodborne pathogenic bacter...

Artificial intelligence-based molecular property prediction of photosensitising effects of drugs.

Journal of drug targeting
Drug-induced photosensitivity is a potential adverse event of many drugs and chemicals used across a wide range of specialties in clinical medicine. In the present study, we investigated the feasibility of predicting the photosensitising effects of d...

Intra- and inter-channel deep convolutional neural network with dynamic label smoothing for multichannel biosignal analysis.

Neural networks : the official journal of the International Neural Network Society
Efficient processing of multichannel biosignals has significant application values in the fields of healthcare and human-machine interaction. Although previous research has achieved high recognition performance with deep convolutional neural networks...