Machine learning approach for enumeration of circulating cells with diffuse

Significance: Diffuse Aim: Our aim is to develop a machine learning (ML)-integrated signal processing approach for DiFC that improves CTC enumeration by better discriminating CTC peaks from artifacts. Approach: We developed an ML-integrated approach that incorporates a convolutional neural network (CNN) classifier. The CNN was trained to distinguish CTC peaks from artifacts by analyzing peak amplitude and temporal shape characteristics. Performance was evaluated on Results: The CNN classifier achieved accuracy, precision, sensitivity, and specificity exceeding 94% on test data. Compared with our previously published threshold-based approach, the ML-integrated method increased the number of correctly identified CTCs and their flow direction while reducing false detections across evaluation datasets. Conclusions: The ML-integrated approach substantially improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.