## 4 Discussion
Therefore, the simplest way to prevent the overfitting is to reduce the size of the model, i.e., the number of learnable parameters in the model, which is determined by the number of layers and units per layer. By probabilistically selecting the units of each layer of the multi-layer network and invalidating the units other than the selected ones, it is possible to create a \"temporary network with a small degree of freedom\". By training with this process, the effect of simplifying the model can be obtained. (3) Regularization  
There is a method of adding a regularization term to the error function to suppress the complexity and degree of freedom of the model and prevent overfitting. The most common regularizations are L1 regularization and L2 regularization. With L1 regularization, some parameters can be set to zero, which is similar in feature selection, resulting in a sparse model. Furthermore, if there are many zero, it can be expressed as a sparse matrix and can be calculated at high speed. On the other hand, L2 regularization is a method of approaching zero according to the size of the data and is characterized by being able to build a smooth model. Thus, while DL has grown into a program that can produce amazing output through long-term operation, proper tuning and verification by human must be continued to avoid the overfitting, because the overfitting can be prevented depending on the approach, and even after it occurs, DL can be updated to a normal model by troubleshooting in the correct way. Further, another of the challenges facing machine learning (ML) is the selection of appropriate ML models for classifying domain-specific data. Some of the main ML techniques are:  
(1) Support vector machine (SVM)  
The SVM is a model of unsupervised learning that can handle both analysis and regression, and mainly solves classification tasks, and can make predictions with high accuracy even for unknown data by maximizing the margin that draws a boundary so that the distance of data near the boundary is maximized. (2) K-nearest neighbor (kNN) algorithm  
The kNN method plots the given training data on the vector space, and if unknown data is obtained, acquire any k pieces in order of distance from them, and estimate the class to which the data belongs by the majority vote. (3) Naive Bayes classifier  
This method is classifier to determine which category a certain data belongs to and makes predictions according to Bayesian discrimination rules based on a simple stochastic model. In addition, it was reported that comparison of the DL and traditional ML models for classification of normal and abnormal cells by using seven different ML algorithms such as logistic regression, kNN, SVM, naive Bayes, random forest, XGBoost, and deep neural network. Of these algorithms, deep neural network indicated outperformance compared with other algorithms [83]. One of the major concerns with the manufacture of transplanted cells used in regenerative medicine is the risk of cross-contamination, which causes accidental mixing of cell samples. Control of the manufacturing process requires eliminating the existence of harmful impurities such as cancer cells and ensuring the quality of the transplanted cells. A soft agar colony formation (SACF) test is commonly used to evaluate the anchorage-independent growth, which is one of characteristics of malignant transformed cells such as cancer cells. This test can detect the presence of abnormal cells in normal cells in a short time. Our group [9] developed a digital assay screening system that detects cancer cells among normal cells with better sensitivity and efficiency than the conventional SACF test. However, this approach requires a skillful technique to make multiple layers of viscous agar medium with different concentrations in many wells.