## 6 Discussion  
This study revealed that the method of training the CNN model with lung cell images and then fine-tuning with urinary cell images was more accurate than training with urinary cell images. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**(a) Method 1**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{3-5} \multicolumn{2}{c}{} & **Benign** & **Malignant** \\ \hline \multirow{2}{*}{Actual} & Benign & 431 & 285 \\ \cline{2-5}  & Malignant & 125 & 539 \\ \hline \multicolumn{2}{c}{**(b) Method 2**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{3-5} \multicolumn{2}{c}{} & **Benign** & **Malignant** \\ \hline \multirow{2}{*}{Actual} & Benign & 663 & 53 \\ \cline{2-5}  & Malignant & 29 & 635 \\ \hline \multicolumn{2}{c}{**(c) Method 3**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{3-5} \multicolumn{2}{c}{} & **Benign** & **Malignant** \\ \hline \multirow{2}{*}{Actual} & Benign & 703 & 13 \\ \cline{2-5}  & Malignant & 8 & 656 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrices of the three classification methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1 Score** \\ \hline Method 1 & 0.812 & 0.602 & 0.654 & 0.703 & 0.724 \\ Method 2 & 0.956 \(\pm\) 0.014 & 0.926 \(\pm\) 0.058 & 0.923 \(\pm\) 0.055 & 0.941 \(\pm\) 0.028 & 0.939 \(\pm\) 0.027 \\ Method 3 & 0.988 \(\pm\) 0.019 & 0.982 \(\pm\) 0.034 & 0.981 \(\pm\) 0.041 & 0.985 \(\pm\) 0.027 & 0.984 \(\pm\) 0.030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of evaluation index for image classification. Figure 3: Correctly classified and misclassified images and their activation maps. Benign cells correctly classified (**A–C**) and misclassified (**D–F**) images in Methods 1–3, respectively. Same for the Malignant cells: correctly classified (**G–I**) and misclassified (**J–L**) images. Papanicolaou staining (left panel) and activation map by Grad-CAM (right panel) in each photo. cell images alone. In clinical practice, urinary cytology is determined based on cellular characteristics such as increased nuclear chromatin, increased nuclear-cytoplasmic ratio (N/C ratio), and nuclear enlargement [21], which are often applied in lung cytology as well. The malignant-lung-cell images used in this study contained two histologically different tumor cells: squamous cell carcinoma and adenocarcinoma. The common characteristics of these cells are increased nuclear chromatin, increased N/C ratio, and nuclear enlargement. Other features include nucleolus clarity, flow-like arrangement, and the presence of bright cells in squamous cell carcinoma; and nucleolus clarity, enlargement, nuclear maldistribution, and mucus production in adenocarcinoma [21]. In this study, liquid-based cytology specimens stained using Papanicolaou were prepared from urinary cells collected from spontaneous urine. In this process, detached urothelial cells in the urine are prone to degeneration, forming degenerated normal urothelial cells called reactive urothelial cells. The degeneration is caused by a variety of factors, including a high degree of inflammation, the presence of stones, drug and radiation therapy, early morning urine, and mechanical manipulation with catheterized urine. Degenerated urinary cells are characterized by nuclear enlargement and clear nucleoli compared to those of normal cells, and it is difficult to distinguish them from malignant cells. In actual clinical practice, the distinction is made based on the presence or absence of an irregular nuclear shape and increased nucleochromatin, which requires advanced techniques. To solve this problem, this study proposed an automated method for the more accurate classification of urinary cytology. Individual cell-based judgement should further evolve to case-based diagnosis by using all of the cells, which could collaborate with the actual diagnostic process. The results of the evaluation showed that Method 3 was the most accurate among all methods.