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• W4313555986 abstract "Automatic cell counting provides an effective tool for medical research and diagnosis. Currently, cell counting can be completed by transmitted-light microscope, however, it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells. Further, the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope, automatically and effectively. In this work, a new deep-learning (DL)-based two-stage detection method (cGAN-YOLO) is designed to further enhance the performance of cell counting, which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model. The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images. Compared with the previously reported YOLO-based one-stage detection method, high recognition accuracy (RA) is achieved by the cGAN-YOLO method, with an improvement of 29.80%. Furthermore, we can also observe that cGAN-YOLO obtains an improvement of 12.11% in RA compared with the previously reported image-translation-based detection method. In a word, cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance, which extends the applicability in clinical research." @default.
• W4313555986 created "2023-01-06" @default.
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• W4313555986 date "2023-02-18" @default.
• W4313555986 modified "2023-10-14" @default.
• W4313555986 title "Deep learning method for cell count from transmitted-light microscope" @default.
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• W4313555986 doi "https://doi.org/10.1142/s1793545823500049" @default.
• W4313555986 hasPublicationYear "2023" @default.
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