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Papers
61,005 resultsShowing papers similar to Deep Learning Approaches for Real-Time Medical Image Segmentation
ClearDetection of Microplastic Ingestion in the Human Body Using Deep Learning Technique
Researchers applied convolutional neural networks trained in MATLAB to detect and quantify microplastic contamination in high-resolution tissue images, demonstrating that deep learning can automate the identification of plastic particles in biological samples.
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.
Detection 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.
Wet Poster Session
This thesis focused on medical imaging diagnostics using deep learning, proposing methods to handle uncertainty in neural network predictions for safer clinical use. It has no relevance to microplastics or environmental health.
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.
Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source
Researchers developed a real-time instance segmentation system using neural networks to detect underwater plastic litter on the seafloor, targeting the approximately 70% of marine litter that sinks and serves as a major source of ocean microplastics.
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.
Development of representative convolutional neural network based models for microplastic spectral identification
Researchers developed more representative convolutional neural network (CNN) models for microplastic spectral identification by training on expanded spectral databases that include greater diversity of plastic types, aging stages, secondary additives, pigments, and environmental contamination, outperforming library-search methods in classification accuracy and speed.
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.
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.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Sizing Microplastic Particles Using Acoustic Imaging and Deep Neural Network
Researchers developed an acoustic imaging-based deep neural network system to size microplastic particles in real time, comparing Total Focusing Method and Circular Wave Imaging strategies and achieving accurate particle segmentation from acoustic signals.
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.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
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.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
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.
Detection of Microplastics in Coastal Environments Based on Semantic Segmentation
Researchers developed a deep learning semantic segmentation approach for detecting microplastics on sandy beaches at the pixel level, evaluating 12 models including U-Net variants and transformer architectures under real-world conditions.
Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network
Researchers developed a deep-supervised full-resolution residual network for automatic segmentation of COVID-19 pneumonia lesions in CT images, achieving high accuracy in delineating ground-glass opacities and consolidations to assist clinical diagnosis.
AI-Enhanced Patient-Derived Cancer Organoids: Integrating Machine Learning for Precision Oncology
This review explores how combining patient-derived cancer organoids with artificial intelligence enables more precise drug sensitivity predictions and biomarker discovery in oncology research. While not directly related to microplastic research, the study demonstrates how AI and advanced biological models can be integrated to analyze complex datasets. The approaches described may inform future methods for studying how environmental contaminants interact with human tissues.
Litter segmentation with LOTS dataset
Not a microplastics paper — this computer science paper presents a machine learning benchmark for detecting and segmenting beach litter (including plastic debris) in sand using deep learning image segmentation models, contributing tools that could help automate coastal pollution monitoring.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Researchers developed a deep learning model using a convolutional neural network with spatial attention to classify microplastics mixed with natural organic matter from Raman spectra. The model achieved 99.54% accuracy compared to just 31.44% from conventional spectral library software, demonstrating that AI-based approaches can dramatically improve microplastic identification accuracy while reducing the need for time-intensive preprocessing steps.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.