MicroDetect-Net (MDN): Leveraging Deep Learning to Detect Microplastics in Clam Blood, a Step Towards Human Blood Analysis

TL;DR

This paper introduces MicroDetect-Net, a deep learning model combined with fluorescence microscopy to detect microplastics in blood samples, showing high accuracy and promising potential for human health monitoring.

Contribution

The study presents a novel deep learning approach using fluorescence imaging and Nile Red dye for microplastic detection in blood, advancing methods for environmental and health research.

Findings

Achieved 92% accuracy in microplastic detection

Demonstrated high precision and recall metrics

Validated effectiveness on a dataset of 276 images

Abstract

With the prevalence of plastics exceeding 368 million tons yearly, microplastic pollution has grown to an extent where air, water, soil, and living organisms have all tested positive for microplastic presence. These particles, which are smaller than 5 millimeters in size, are no less harmful to humans than to the environment. Toxicity research on microplastics has shown that exposure may cause liver infection, intestinal injuries, and gut flora imbalance, leading to numerous potential health hazards. This paper presents a new model, MicroDetect-Net (MDN), which applies fluorescence microscopy with Nile Red dye staining and deep learning to scan blood samples for microplastics. Although clam blood has certain limitations in replicating real human blood, this study opens avenues for applying the approach to human samples, which are more consistent for preliminary data collection. The MDN model integrates dataset preparation, fluorescence imaging, and segmentation using a convolutional neural network to localize and count microplastic fragments. The combination of convolutional networks and Nile Red dye for segmentation produced strong image detection and accuracy. MDN was evaluated on a dataset of 276 Nile Red-stained fluorescent blood images and achieved an accuracy of ninety two percent. Robust performance was observed with an Intersection over Union of 87.4 percent, F1 score of 92.1 percent, Precision of 90.6 percent, and Recall of 93.7 percent. These metrics demonstrate the effectiveness of MDN in the detection of microplastics.