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Detection of Microplastic Ingestion in the Human Body Using Deep Learning Technique
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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.
This study investigates using Neural Networks with convolution(CNNs) MATLAB to detect microplastic contamination in human tissues. As microplastics threaten health and ecosystems, this study provides a CNN-based method for their precise identification and quantification in high-resolution tissue images. This approach includes image preprocessing and CNN optimization to detect microplastic characteristics accurately. The effectiveness of model is confirmed through key performance metrics. Results advance deep learning in environmental health and aid in understanding and preventing microplastic-related health risks.
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Detection of Micro Plastics in Human Lung Tissues: Using Matlab-Based CNN
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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
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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
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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
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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
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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.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.