Label-free cell classification and screening system based on hybrid transfer learning

A label-free cell classification and screening system based on hybrid transfer learning, comprising:

• i) a data preprocessing circuit, which is configured to:
• acquire 2D light scattering video data, preprocess the 2D light scattering video data to obtain images after removing disturbance;
• ii) an image archiving circuit, which is configured to:
• sort and label the images to get labeled images according to a ground truth;
• iii) a feature extraction circuit, which is configured to:
• get feature vector of the labeled images using a first convolutional neural network with pre-trained parameters;
• and
• iv) a cell classification and screening circuit, which is configured to:
• input the feature vector into a trained support vector machine model to get cell classification results;
• wherein,
• the support vector machine model is trained with feature vectors of clinical samples and transferred feature vectors of cell-lines;
• preprocessing the 2D light scattering video data, including a digital cell filtering technique, including:
• videos of the 2D light scattering video data are divided into the images data frame by frame, and then the images are filtered;
• each image of the images is processed by morphological granularity analysis algorithm to obtain an image morphological granularity characteristic value;
• determine whether a feature value meets a pre-set standard, if so, keep the image, otherwise remove the image;
• and
• a trained machine learning model is used to further filter the images.