# Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models
## 5 Discussion
This suggests that combining CNN models with different layers in our proposed tandem combination may be effective for improving accuracy. Most ensemble methods involve a parallel connection of multiple AI models for the same leukocyte classification number at the final process, whereas our method involves a tandem connection of a rough leukocyte classification model and a fine identification model limited to a specific cell category. However, both studies showed similar results in terms of improved accuracy by combining multiple AI models, which supports the validity of our proposed method. Our proposed method limits the leukocyte classification categories to a small number of morphologically similar categories, which we presume allows for the detection of features that are valid only for identification between specific cells. Despite the same training data, the number of A-lymph detections increased from 720 to 822 with the two-step model; therefore, it is clear that a differently weighted AI model was generated. Similarly, the number of misclassifications from A-lymph to Lymph decreased from 361 to 192 with the two-step model, while the number of misclassifications to Mono increased from 37 to 104; therefore, it is clear that different features were recognized. However, while improved sensitivity can be useful in terms of preventing missed cells, over-detection by automated technologies may lead to the need for retesting by experts. Therefore, we consider that this model needs improved precision for atypical lymphocytes in terms of efficiency. Concerning the causes of over-detection in clinical assessments, some trends have been found in cell classification (Figure 6). Our results indicated that A-lymph with strong cytoplasmic basophilia or A-lymph with moderate cytoplasmic basophilia and intense atypical nuclear changes (appearance of many nucleoli or agglutinated nuclei) was able to be classified accurately (Figure 7A,B). By contrast, normal large Lymph with mild cytoplasmic basophilia but without atypical nuclear changes was misclassified into A-lymph, and A-lymph with mild cytoplasmic basophilia and atypical nuclear changes was misclassified into normal Lymph by the AI model (Figure 7C,D). Identifying atypical lymphocytes is based on a comprehensive rating of two factors: nuclear structure and the level of basophilia of the cytoplasm. It is presumed that plasma cell- or lymphoblast-like A-lymph could be easily identified from normal lymphocytes because these cells have strong cytoplasmic basophilia. On the contrary, Mono, normal large Lymph, and monocyte-like A-lymph show remark ably similar morphology. It is suggested that misclassification may occur with moderate cytoplasmic basophilia in these borderline morphological cases. Therefore, it may be necessary to consider cutoff values for average basophilic levels and to reconstruct the data set for AI training based on Downey subclassification to obtain high accuracy and precision. This is supported by [PERSON], [PERSON] et al. [31], who reported that setting cutoff values to maximize sensitivity and specificity improves the accuracy of AI analysis. In addition, the establishment of borderline classifications may also be useful in limiting the number of cells to be retested by experts. Malignant cell screening performed as a noninvasive test of solid tumors (e.g., uterine cancer, lung cancer, or urologic tumors) in cytopathology is a representative example. The Papanicolaou Classification or Bethesda System is used as a grading system in cancer cell screening [32; 33; 34]. These grading systems contain a malignant suspect category, intermediate between normal and malignant, which allows retests to be limited to borderline cells. The current visual blood morphology classification does not define \"suspicious\" as a separate category and differs from the malignant cell classification in cytopathological diagnosis.