# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
###### Abstract  
Colorectal cancer is one of the most common cancers with a high mortality rate. The determination of microsatellite instability (MSI) status in resected cancer tissue is vital because it helps diagnose the related disease and determine the relevant treatment. This paper presents a two-stage classification method for predicting the MSI status based on a deep learning approach. The proposed pipeline includes the serial connection of the segmentation network and the classification network. In the first stage, the tumor area is segmented from the given pathological image using the Feature Pyramid Network (FPN). In the second stage, the segmented tumor is classified as MSI-L or MSI-H using Inception-Resnet-V2. We examined the performance of the proposed method using pathological images with 10\(\times\) and 20\(\times\) magnifications, in comparison with that of the conventional multiclass classification method where the tissue type is identified in one stage. The F1-score of the proposed method was higher than that of the conventional method at both 10\(\times\) and 20\(\times\) magnifications. Furthermore, we verified that the F1-score for 20\(\times\) magnification was better than that for 10\(\times\) magnification. color=blue, outerline=0.5 em, innerline=0. Therefore, pathologists are strongly recommended to check the status of MSI in resected CRC tissue [5]. A general diagnosis procedure is conducted by examining hematoxylin- and eosin-stained CRC tissue and manually checking any abnormalities in the microscopic images. However, this procedure is tedious, and sometimes, prone to error. Automatic diagnosis based on pathological images, initially proposed for breast cancers, has been widely studied to reduce the fatigue of pathologists [6]. In recent years, deep-learning-based approaches have been proposed to diagnose different cancers; moreover, they have exhibited good results, comparable to those of manual diagnoses. These studies focused on two main tasks for the pathological diagnosis: segmentation and classification. Prior to the widespread use of deep learning approaches, various segmentation methods have been studied: region-based [7; 8; 9] and edge-based approaches [10]. Region-based segmentation methods are generally more robust than edge-based methods, particularly on images containing noise and texture; however, they are not accurate in determining the boundary of an object in real-world images. Segmentation methods using deep learning have outperformed the prior methods both in determining the boundaries and processing the images with noise and texture; therefore, they have been widely used on medical images [11]. The fully convolutional network (FCN) is the originator of the semantic segmentation network [12], which has been adapted to vessel segmentation [13], skin lesion segmentation [14], and organ segmentation from computed tomography scans [15]. UNet, originally developed for neuronal structure segmentation in electron microscopic stacks [16], is widely used in medical image segmentation, demonstrating good results on 2D and 3D images [17; 18]. Image recognition competitions such as ILSVRC [19] have brought a dramatic improvement in image classification networks. For example, the famous networks such as AlexNet [20], VGG [21], and Resnet [22] were developed through competitions. VGG demonstrates that deeper networks have better classification performances. GoogleNet, often referred to as InceptionNet, uses the Inception block to form a more complex network with a smaller number of parameters. The skip connection of Resnet enables the formation of extensively deep networks. These deeper and more complex networks have been widely used for different medical image classification tasks by achieving performance comparable to that of medical experts. Inception-V3 has been adopted to diagnose skin cancer [23] and detect diabetic retinopathy [24]. AlexNet has been applied to classify thyroid cytopathology [25].