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Papers
20 resultsShowing papers similar to Detection of Microplastic Ingestion in the Human Body Using Deep Learning Technique
ClearDetection of Micro Plastics in Human Lung Tissues: Using Matlab-Based CNN
This study developed a MATLAB-based convolutional neural network approach to detect microplastics in human lung tissue images from CT or microscopy scans. The system combined image enhancement, region-of-interest extraction, and CNN classification to identify plastic particle presence with high detection accuracy.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study
Researchers developed a label-free method to identify microplastics inside living human cells using enhanced dark-field microscopy combined with deep learning, achieving high classification accuracy for polystyrene microparticles differing only in pigmentation.
Automated Plastic Waste Detection Using Advanced Deep Learning Frameworks
Researchers developed a deep learning system using advanced neural network frameworks for automated detection and classification of plastic waste from images, achieving high accuracy in identifying multiple plastic types to support environmental monitoring and waste sorting.
Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning
Researchers developed a deep learning system that can automatically detect and classify microplastics in scanning electron microscope images, replacing the time-consuming process of manual analysis. The system achieved high accuracy in identifying different types and shapes of microplastic particles, even very small ones that are difficult to spot by eye. This automated approach could significantly speed up microplastic monitoring and pollution assessment efforts.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Use of a convolutional neural network for the classification of microbeads in urban wastewater
Researchers developed a convolutional neural network model to classify and identify microbeads from cosmetic products in urban wastewater, demonstrating that deep learning approaches can provide a practical and scalable standard for automated microplastic characterization in water treatment contexts.
Detection of microplastics in fish using computed tomography and deep learning
CT scanning combined with deep learning neural networks enabled non-destructive, automated detection and localization of microplastics in fish with high accuracy, overcoming the contamination risk and time-consuming nature of conventional dissection-based methods.
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
Researchers created a new open-source dataset of microscopy images for training AI models to automatically detect and classify micro- and nanoplastics. The dataset fills an important gap in available tools for microplastic research and provides a foundation for developing faster, more efficient methods to identify plastic contamination across environmental samples.
Automated micro-plastic detection and classification using deep convolution neural network pre-trained models and transfer learning
Researchers compared several artificial intelligence models for automatically detecting and classifying microplastics into categories like beads, fibers, and fragments from images. While the models performed well at identifying fiber-type microplastics, they struggled with beads and fragments, highlighting the need for better training data and techniques. Improving automated detection is important because it could enable faster, cheaper environmental monitoring of microplastic contamination in water and food sources.
Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
Researchers developed an innovative method combining photoacoustic imaging with deep learning to rapidly detect and characterize microplastics. The photoacoustic technology captured high-resolution images of diverse microplastic samples, while the neural network automated the classification process. The study demonstrates that this combined approach could enable faster, more accurate microplastic monitoring compared to conventional methods.