HIGHLIGHTS
SUMMARY
Current approaches for detecting stained CTCs range from manual scoring to techniques based on image processing and machine_learning. There is a growing interest in using machine_learning to detect and classify cells in patient blood samples as it eliminates drawbacks of manual scoring and threshold-based object detection. As a first step, DL-based frameworks are being pursued where in_vitro cancer cells are mixed with blood cells, and the efficacy of DL models is being investigated to detect live cancer cells in a background of blood cells35-38 The authors utilize CNN approaches to demonstrate . . .
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