AIMC Topic: Deep Learning

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Deep learning-based prediction of compressive strength of eco-friendly geopolymer concrete.

Environmental science and pollution research international
The greenhouse gases cause global warming on Earth. The cement production industry is one of the largest sectors producing greenhouse gases. The geopolymer is produced with synthesized by the reaction of an alkaline solution and the waste materials s...

MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.

Frontiers in cellular and infection microbiology
While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based ...

Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT.

Academic radiology
RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images w...

A new artificial intelligence system for both stomach and small-bowel capsule endoscopy.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking.

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins.

Computational biology and chemistry
Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on informa...

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Medical physics
BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commerc...

Automated Lugano Metabolic Response Assessment in F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on F-Fluorodeoxyglucose-Positron Emission Tomography.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.

Deep learning-based flocculation sensor for automatic control of flocculant dose in sludge dewatering processes during wastewater treatment.

Water research
In sludge dewatering of most wastewater treatment plants (WWTPs), the dose of polymer flocculant is manually adjusted through direct visual inspection of the flocs without the aid of any instruments. Although there is a demand for the development of ...

Prediction of the short-term efficacy and recurrence of photodynamic therapy in the treatment of oral leukoplakia based on deep learning.

Photodiagnosis and photodynamic therapy
BACKGROUND: The treatment of oral leukoplakia (OLK) with aminolaevulinic acid photodynamic therapy (ALA-PDT) is widespread. Nonetheless, there is variation in efficacy. Therefore, this study constructed a model for predicting the short-term efficacy ...