Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics.

ABSTRACT: The shape of microplastics (MPs) matters. Yet, the expert-based shape classification is labor intensive, time consuming, and susceptible to human biases. In this study, we investigated deep learning-based approaches for automating shape classifications of MP hyperspectral images, thus achieving a faster and more accurate classification procedure. Here, nine deep learning architectures (NN1.1, NN 1.2, CNN 1.1, CNN 1.2, CNN 1.3, VGG16, ResNet50, ResNet50 V2, and MobileNet) were tested and further compared in terms of their performance discrepancies across four data sets (original, augmented original, refined, and augmented refined data sets). Our sample images comprise the hyperspectral images of 11,042 environmental MP, (particle sizes down to 10 μm) analyzed with micro-Fourier transform in

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12489888 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature