1. Introduction
The incidence of kidney diseases has recently undergone considerable
changes worldwide. Among these diseases, chronic kidney disease needs to
be particularly researched1. It is noteworthy that
most patients with kidney disease are early patients, having more
treatment opportunities than those with advanced renal insufficiency.
Regular screening allows patients to receive treatment in a timely
manner to prevent and control kidney damage. The analysis and diagnosis
of clinical urinary particles are important for screening and preventing
kidney as well as related diseases2,3, because it can
provide information about the type and quantities of cells in the urine,
which, in turn, can provide a scientific basis for diagnosis by doctors.
Therefore, it is crucial to accurately analyze and detect the types and
quantities of cells in urinary particles.
Urinary particles are formed from the urinary tract and refer to
substances formed by exudation, discharge, shedding, and concentration
of crystals in visible form4. Common urine cells
include erythrocytes, leukocytes, epithelial, casts, crystals, bacteria,
fungi, etc.5,6. Among them, erythrocytes, casts, and
crystals can be subdivided into several types. Depending on the type and
number of cells, each component has a clear pathological significance.
For example, erythrocytes in normal urine are invisible; however, when
they appear in urine, it may indicate pathological bleeding in the
urinary system. Common examination methods for urinary particles are
microscopy7, SM staining8 and
morphological examination. Among them, microscopy is the “gold
standard” for urine analysis9 and can accurately
detect cells in urine. The SM staining method requires staining of a
urine smear, and the detection accuracy is further improved by using
microscopy in combination with SM staining. However, the first two
methods require manual operation, which is time consuming and labor
intensive, and cannot meet the actual needs of clinical tests and the
detection of automated urinary particles.
Urine morphological examination detects different cellular components by
examining the size and shape of the cells under a microscope. According
to different detection algorithms, the method can be roughly divided
into two categories: traditional and new object detection algorithms
based on artificial neural network. A typical object detection algorithm
includes a detection algorithm based on decision tree
classifier10, a support vector machine and template
matching (SVM) 11, and an adaptive discrete wavelet
entropy energy algorithm12. Traditional detection
algorithms require the use of steps, such as object segmentation and
manual design of feature extractors, and the speed and accuracy of
detection need to be improved. With the rise and development of deep
learning13, the object detection algorithm based on
artificial neural network has achieved great success. It mainly includes
a one-stage target detection algorithm represented by
Yolo14,15 and SSD16, a two-stage
target detection algorithm represented by
Faster-RCNN17,18,19, and various variants derived from
these algorithms20,21. Compared with traditional
recognition algorithms, these algorithms have considerably improved the
speed and accuracy of detection.
The object detection algorithm based on deep learning has been applied
for the detection of urinary particles; however, the accuracy of
detection needs to be further improved. In terms of the speed of
detection22, the author pointed out that the slow
detection speed was due to a class imbalance between the object class
and the background class. In the online hard example mining
algorithm23, the author increased the weight of the
misclassified sample but ignored the easily categorized samples.
Therefore, these algorithms have room for improvement in terms of
detection speed. Most importantly, fewer cell types have been detected
in previous studies due to three main reasons: the detection of
multi-class cells is difficult, and it is a huge challenge for network
design and parameter adjustment; as the number of categories increases,
the confusion between cell categories increases, ultimately affecting
the performance of the model; owing to the small size of cells and the
large differences in the cell characteristics, character-stabilized,
easily identifiable cells have been studied. However, the clinical
significance of other categories is also huge and should be considered.
Based on the above discussion, we propose a method for analyzing and
detecting urinary particles using multi-class fine classification based
on deep learning. Compared with the previous urine object detection
method, we have added many other rare but important categories of
clinical significance that will not affect the accuracy and speed of
detection, allowing quick and easy detection of multiple categories of
urine cells and providing doctors with more abundant disease
information, which can be of great significance for the clinical
examination of urinary particles.