Abstract
Microscopic-Diffraction Imaging Flow Cytometry (MDIFC) is a high-throughput, stain-free technology that captures paired microscopic and diffraction images of cellular events, utilizing machine learning for the classification of cell subpopulations. However, MDIFC is still hindered by challenges related to limited accuracy, processing speed, and a lack of automation. To address this, we propose a novel approach that integrates image fusion techniques with a deep learning-based classification algorithm. Using budding yeast recognition as a model system, we categorized events into three groups: single cells, budding cells, and aggregated cells. Paired images were fused with varying weight factors to generate a comprehensive training dataset for a VGG-net-based Convolutional Neural Network (CNN). For comparison, Support Vector Machines (SVM) and Random Forests (RF) based on Grey-Level Co-occurrence Matrix (GLCM) features were employed. The results demonstrate that the VGG-net classifier achieved an impressive classification accuracy of 0.98 when trained on a dataset with a fusion weight of 0.2 for microscopic images and 0.8 for diffraction images. Furthermore, it demonstrated a high throughput of 260.42 cells per second, surpassing the performance of GLCM-based methods. These findings suggest that the combination of image fusion and deep learning algorithms significantly improves both the speed and accuracy of cell classification in MDIFC, offering substantial benefits for high-throughput cell analysis in biological and medical applications.