AIMC Topic: Deep Learning

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Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.

European radiology experimental
BACKGROUND: Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-w...

AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis.

Scientific reports
Capillary microfluidic chips (CMCs) enable passive liquid transport via surface tension and wettability gradients, making them central to point-of-care diagnostics and biomedical sensing. However, accurate analysis of capillary-driven flow experiment...

Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Health Care System Administration using Deep Reinforcement Learning.

Disaster medicine and public health preparedness
OBJECTIVES: Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential...

Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction.

PloS one
Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dippe...

Efficient wheat variety identification using Raman hyperspectral imaging in combination with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat (Triticum aestivum L.) is recognized as a globally important staple crop, with its varietal differences influencing food processing, nutritional value, and agricultural productivity. Traditional identification methods are often considered ineff...

Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device.

Scientific reports
Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but trad...

DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit.

BMC bioinformatics
BACKGROUND: Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) tec...

Advancements in deep learning-based image screening for orthopedic conditions: Emphasis on osteoporosis, osteoarthritis, and bone tumors.

Ageing research reviews
Artificial intelligence (AI) has garnered increasing attention in the medical field. As the core technology of AI, deep learning (DL) has been extensively applied to the imaging-based screening of orthopedic diseases, primarily including image classi...

Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.

Scientific reports
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inher...

A novel hybrid convolutional and transformer network for lymphoma classification.

Scientific reports
Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subty...