AIMC Topic: Deep Learning

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An enhanced deep learning approach for speaker diarization using TitaNet, MarbelNet and time delay network.

Scientific reports
Speaker diarization, identifying "who spoke when," plays a vital role in speech transcription, supervised fine-tuning of large language models, conversational AI, and audio content analysis by providing labeled speaker segments. Traditional speaker d...

Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI.

Scientific reports
Robust differentiation between infarcted and normal myocardial tissue is essential for improving diagnostic accuracy and personalizing treatment in myocardial infarction (MI). This study proposes a hybrid framework combining radiomic texture analysis...

Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50.

Scientific reports
Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health consequences and economic burden that strokes can cause. In this study, we proposed a class-balanced and dat...

Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.

Scientific reports
Early diagnostic assessments of neurodivergent disorders (NDD), remains a major clinical challenge. We address this problem by pursuing the hypothesis that there is important cognitive information about NDD conditions contained in the way individuals...

Motor imagery EEG signal classification using novel deep learning algorithm.

Scientific reports
Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges ...

Unveiling aging heterogeneities in human dermal fibroblasts via nanosensor chemical cytometry.

Nature communications
Aging heterogeneity in tissue-regenerative cells leads to variable therapeutic outcomes, complicating quality control and clinical predictability. Conventional analytical methods relying on labeling or cell lysis are destructive and incompatible with...

Monochromatic LeafAdaptNet (MLAN): an adaptive approach to spinach leaf disease detection using monochromatic imaging.

World journal of microbiology & biotechnology
A country's economic growth heavily relies on agricultural productivity, specifically nutrition derived from vegetables and leafy greens. Spinach, abundant in iron, vitamins, and other essential nutrients, plays a vital role in maintaining the health...

Deep learning-based allergic rhinitis diagnosis using nasal endoscopy images.

Scientific reports
Allergic rhinitis typically has edematous and pale turbinates or erythematous and inflamed turbinates. While traditional approaches include using skin prick tests (SPT) to determine the presence of AR, It is often not related to actual symptoms, and ...

Plant attribute extraction: An enhancing three-stage deep learning model for relational triple extraction.

PloS one
Various plant attributes, such as growing environment, growth cycle, and ecological distribution, can provide support to fields like agricultural production and biodiversity. This information is widely dispersed in texts. Manual extraction of this in...

Deep learning-assisted Raman spectroscopy for rapid lactic acid bacteria identification at the colony level.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
To achieve rapid and accurate identification at the colony level and improve the efficiency of colony selection, we proposed an adaptive colony Raman acquisition method based on signal-to-noise ratio screening (ACRA-SNR). This method enables in situ ...