4. Discussion
Based on the RetinaNet model technology of deep learning, we established
a multi-class classification method for the detection of urinary
particles. The average accuracy of recognition was 82.86%, and the
detection accuracy of a single category was also high. Compared with
other methods, our method was able to perform multi-class analysis and
detect urine cells. The experimental results showed that the accuracy of
recognition is relatively high, with some categories showing better
accuracy than others; good results were also achieved in terms of speed
performance. However, using experimental data, we found that the
recognition effect of some categories was unsatisfactory. Therefore, we
discuss the specific factors that affect the accuracy of recognition
result through both internal factors and external interference.
In the early stage of model design, the network structure and network
parameters are often determined using empirical values. Different
network parameter configurations will have different effects on the
network model. As shown in Table 2, we discussed the impact of the
following four conditions on accuracy: weight initialization method,
feature extractor selection, anchor size, and loss function parameter
configuration.
The experimental results demonstrated that the accuracy of the model was
higher in the COCO weight initialization mode. The experimental results
of Resnet50 and Resnet101 basic networks were very close; however,
considering the Resnet101 network model is deep and more complex, we
chose the Resnet50 basic network. In addition, anchors with a smaller
size can help in improving the accuracy of the method. In the parameter
selection of loss function, we observed that when αt was 0.25 and γ was
2.0, the effect was better.
Based on the above discussion, we chose a satisfactory model parameter
configuration. In addition, medical images are expensive to label, and a
lack of pathological samples could result in a lack of sample data in
certain categories. However, deep learning is based on multi-dimensional
data extraction and analysis of big data. The lack of data could
fundamentally affect the accuracy of model testing. In this study, due
to insufficient data regarding uric acid crystals, low-transitional
epithelium, and abnormal erythrocyte, the recognition accuracies of
these types are low, thus affecting the accuracy of the method.
In order to compare with other methods, we compare the optimized model
results with two other typical methods. The comparison results are shown
in Table 3. The results show that our accuracy rate is higher than the
other two methods. This method also has advantages in processing a
single image. Therefore, this method is very helpful for the clinical
diagnosis and automated detection of urinary particles.
When the focus of the objective lens is not clear, poor image quality
could lead to recognition errors or missing recognition. In order to
explore this influencing factor, we artificially changed the focus of
the objective lens and photographed four sets of images using different
sharpness in the same field of view. The acquired image was input into
the model for object detection. The detection effect diagram is shown in
Figure 5a, Figure 5b, Figure 5c and Figure 5d. As observed in the
figure, as the degree of blurring of the image deepens, the situation
regarding the leak recognition becomes more serious. Compared with
Figure 5a, there is a significant difference in the sharpness of the
image, and many category recognition errors appear in Figure 5d.
Therefore, beyond a certain range, the quality of the image can also
affect the accuracy of recognition.
Similar to focus blur, when the shape of the cell changes (during cell
degradation) the data characteristics of the cell change, affecting the
accuracy of identification. Therefore, we should conduct timely
processing of the detected sample or perform human interference (such as
refrigeration of the sample) to prevent degradation.
Due to the characteristics of easy adhesion between cells, the phenomena
of overlap, stacking, and even agglomeration occur, causing leakage
recognition of the model and affecting the detection results of the
model. As shown in Figure 6a, Figure 6b, due to the stacking of cells,
the recognition algorithm treated it as a single cell, whereas others
were not recognized. This is a flaw of the method. Thus, the algorithm
has more room for improvement and research.