# Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification
## 1 Introduction  
Acute kidney injury is a common and significant complication. And it has been proven that one of the factors that cause the disease to occur frequently is the increase in the widespread distribution of red blood cells and excessive inflammation. [1, 2]. The pathophysiological mechanisms that explain the association between increased RDW scores and poorer prognosis remain unclear. According to current knowledge, an increase in RDW is the cause of microcirculation disorders. Older erythrocytes gradually lose the ability to damage cell membranes. This feature is especially important during the squeezing of nucleated cells through small diameter vessels in organs, such as the kidneys. The stiff and large erythrocytes observed in patients with high values of RDW could not enter through the capillaries and thus impaired blood flow through the microcirculation, leading to ischemia of the renal tissue [1]. Recently, kidney disease is increasingly being found in the spread of the coronavirus during the current pandemic [3]. The identification of the host of the second angiotensin-converting enzyme (ACE2) is the first step in the entry of viral infections into the body, where this host leads to cell fusion and entry into host cells in the lungs. Other epithelial cells, such as renal cells, have a high ACE2 expression [4]. As a result, several studies have shown that the long-term consequences of COVID-19 infection may cause COVID-19 patients to develop chronic kidney disease. The antigen capture technique showed that an increase in the SARS-CoV-2 protein in urine samples was detectable when compared to patients before the epidemic. By considering this condition, kidney disease detection needs some prerequisites to obtain the results quickly and accurately. Since COVID-19 is implicated in kidney function, this study aims in identifying how the anatomy of kidney disease is affected by COVID-19. Histopathology revealed that abnormalities inthe kidney occur when the shape of the glomerulus in the kidney structure is different, eventually leading to chronic kidney disease [5]. Understanding how the complex structure of kidney tissue differs, requires the use of imaging technology to compare normal and diseased kidneys. Until recently, there were two types of glomerular tissue disorders: sclerosed and focal-segmental sclerosed [6]. The difference between these two types may be seen in Bowman's capsule structure around the glomerular network. [PERSON]'s visible capsule surrounds the glomerulus, suggesting that the glomerular tissue is undeniably normal, whereas [PERSON]'s capsule that seems faint or even disappears indicates that the glomerular tissue is unhealthy [7]. This explanation is supported by the morphological and Karpinski scores, which indicated the presence of a difference between nucleus and capillary lumens, and the number of areas (mesangial matrix) on the typical glomerular capillary lumens were absent. Moreover, the [PERSON]'s capsule was filled by collagen in glomerular non-healthy kidneys. Focal segmental sclerosis is a disease of the glomerulus that affects many people. A difference in glomerular size, the degree of leg process elimination and the alteration do the clial cells are all signs of this illness [6]. Figure 1 depicts the anatomy distinctions among three types of glomeruli. Imaging technology is predicted to aid in the accurate detection of individuals with renal disease, allowing for better medical performance. Furthermore, the model's ability to analyze data will be determined by how much time is allotted to it. The transfer learning technique used in this study was taken from several machine learning models after reviewing various methods by considering the procedures and work results obtained. [PERSON] et al. [8] used UNet and SegNet to divide the glomerulus into three groups based on segmentation pixels. The data processing findings demonstrate that data prediction from the train data is accurate to 98.16 percent.