5. Conclusion
This paper proposes a multi-class analysis method for the detection of
urinary particles based on deep learning. This method can simply and
quickly detect and classify the cells in urine. Compared with other
methods, our method can detect more cell types in the urine and provide
more effective information for clinical diagnosis. Furthermore, compared
with the artificial method, our method allows the automatic inspection
of urinary particles, saving manpower, materials, and financial
resources. However, in some aspects (such as cell stacking phenomenon),
microscopy still has advantages. Therefore, the algorithm needs further
improvement. In summary, we offer a new approach for the multi-class
clinical examination of urinary particles, and this approach provides a
new approach for other types of clinical cell testing.