AIMC Topic: Deep Learning

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Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Pressure injuries (PIs) pose a negative health impact and a substantial economic burden on patients and society. Accurate staging is crucial for treating PIs. Owing to the diversity in the clinical manifestations of PIs and the lack of ob...

Thyroid nodule classification in ultrasound imaging using deep transfer learning.

BMC cancer
BACKGROUND: The accurate diagnosis of thyroid nodules represents a critical and frequently encountered challenge in clinical practice, necessitating enhanced precision in diagnostic methodologies. In this study, we investigate the predictive efficacy...

Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.

BMC infectious diseases
The daily surge in cases in many nations has made the growing number of human monkeypox (Mpox) cases an important global concern. Therefore, it is imperative to identify Mpox early to prevent its spread. The majority of studies on Mpox identification...

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Communications biology
Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to photosensitivity, small parasite size, and the anisotropy and large refractive index of host erythroc...

Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.

Scientific reports
Applying deep learning algorithms to mine ultrasound features of breast cancer and construct a machine learning model that accurately predicts Ki-67 expression level. This multi-center retrospective study analyzed clinical and ultrasound data from 92...

External validation of artificial intelligence for detection of heart failure with preserved ejection fraction.

Nature communications
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, pa...

Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.

Clinical oral investigations
OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this rela...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...

Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

PloS one
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets.

Computers in biology and medicine
Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a n...