AIMC Topic: Deep Learning

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Explainable deep learning models for predicting water pipe failures.

Journal of environmental management
Failures within water distribution networks (WDNs) lead to significant environmental and economic impacts. While existing research has established various predictive models for pipe failures, there remains a lack of studies focusing on the probabilit...

An artificial intelligence tool that may assist with interpretation of rapid plasma reagin test for syphilis: Development and on-site evaluation.

The Journal of infection
OBJECTIVES: The rapid plasma reagin (RPR) test, a traditional method for diagnosing syphilis and evaluating treatment efficacy, relies on subjective interpretation and requires high technical proficiency. This study aimed to develop and validate a us...

Deep-Learning-Based Analysis of Electronic Skin Sensing Data.

Sensors (Basel, Switzerland)
E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricat...

Comparative Study of Deep Transfer Learning Models for Semantic Segmentation of Human Mesenchymal Stem Cell Micrographs.

International journal of molecular sciences
The aim of this study is to conduct a comparative assessment of the effectiveness of neural network models-U-Net, DeepLabV3+, SegNet and Mask R-CNN-for the semantic segmentation of micrographs of human mesenchymal stem cells (MSCs). A dataset of 320 ...

Deep learning-based classification of dementia using image representation of subcortical signals.

BMC medical informatics and decision making
BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementi...

UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.

BMC medical imaging
Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential...

A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

Scientific reports
This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequenc...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Scientific reports
Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foregrou...

Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.

Scientific reports
Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a vital functional structure in the uterus. The importance of timely recognition cannot be overstated...

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Nature communications
Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a s...