Melanoma is a malignant tumor whose circulating tumor cell (CTC) count has been shown as a prognostic marker formetastasis development. Therefore detection of circulating melanoma cells plays an important role in monitoring tumormetastasis and prevention after diagnosis. In Vivo Photoacoustic Flow Cytometry (PAFC) is established here to achievein vivo melanoma inspection, meanwhile guarantees non-invasive and real-time detection.Accurate tumor cell detectionis of great significance to achieve highly specific diagnosis and avoid unnecessary medical tests.However, the amount ofdata detected by PAFC is large and original photoacoustic signal is mixed with various noises.The traditional triple meansquare deviation method has lower accuracy and consumes a lot of time in data processing. Here, a classificationapproach in photoacoustic is proposed, which could discriminate signals and noises based on features extracted fromphotoacoustic waves compared to normal cells using Support Vector Machines algorithm. Due to similar shape andstructure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and thesignal features in time domain and frequency domain, we finally choose the continuity, amplitude, and photoacousticwaveform pulse width as the features to characterize the signal.More than 600,000 samples were selected as the trainingset (normalized in advance), and a classifier with a precision of 85% accuracy to filter out the photoacoustic signalsrapidly was trained by the support vector machine method.The classifier introduced here has proved to optimize thesignal acquisition and reduce signal processing time, realizing real-time detection and real-time analysis in PAFC.
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