# Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models
## 1 Introduction  
Artificial intelligence (AI) technology is rapidly developing as an automation technology based on human thought processes. Deep learning is one of the machine learning methods in which a computer performs intellectual training for understanding, reasoning, and prediction. Deep learning has multi-layered neural networks that imitate the human cranial nerve circuit by automatically extracting each feature contained in each data category through dozens to hundreds of training iterations. Deep learning detects latent features that humans overlook, making accurate and efficient judgment possible. The continued social implementation of AI technologies, such as automated driving and intelligent agriculture, is expected to contribute to vast improvements in safety and efficiency [1; 2]. Moreover, many efforts have been started in the medical field. Research on medical AI technology is underway to assist diagnostic experts in fields such as radiological, pathological, and endoscopic image diagnoses [3; 4]. Similarly, blood morphology diagnosis is one of the fields in which AI technology is desired to be applied clinically. At present, hematological diseases are diagnosed by performing leukocyte categorization under a microscope on peripheral blood or bone marrow smears. Then, experts (hematologists or clinical laboratory technologists) must identify normal blood cells, reactive blood cells, or neoplastic cells (e.g., leukemia, lymphoma, myelodysplastic syndrome). However, experts require a long training period to master the skill of leukocyte categorization. Therefore, the clinical laboratory strongly desires the practical application of automatic recognition and categorization technology. One representative automation technology for recognition and categorization is an automatic complete blood count (CBC) analyzer, which has fully automatedblood cell counts and categorization. Flow cytometry technology is generally used in almost all CBC analyzers, which have been shown to have high throughput and efficiency in the clinical laboratory, and to be capable of obtaining high data counting accuracy in the case of normal blood cells [5; 6]. However, neoplastic, immature, and reactive blood cells have been reported to show abnormal scattergram patterns, and the accuracy of calculation or categorization is known to be poor [7; 8]. One of the most difficult diseases to differentiate in scattergram pattern analysis is lymphocytes. Lymphocytosis is classified into neoplastic and reactive diseases and may be morphologically divided into polymorphic and monomorphic populations. Monomorphic populations are concerned with lymphoproliferative neoplasm (e.g., acute lymphoblastic leukemia [ALL], chronic lymphoblastic leukemia, or malignant lymphoma). ALL is a representative neoplastic tumor, and lymphoblastic cells have extremely high proliferative ability, so starting clinical treatment early strongly affects the patient's prognosis. By contrast, polymorphic populations are consistent with a reactive process, so this is defined as reactive lymphocytosis [9]. Morphological changes in reactive lymphocytosis are observed as atypical lymphocytes that are activated or juvenilesized by antigenic stimuli from external enemies such as viral infection (e.g., Epstein-Barr virus, cytomegalovirus, or severe acute respiratory syndrome coronavirus 2) [10]. While symptoms are weak in the early stages of viral infection, in the acute phase, when symptoms become apparent, treatment is difficult because the viral load in the body is extremely high. Therefore, the detection of atypical lymphocytes is critical for the early diagnosis and clinical treatment of reactive lymphocytosis associated with viral infection. However, distinguishing normal from atypical lymphocytes for morphological differentiation in hematological testing in the clinical laboratory is often difficult.