## 5 Discussion  
In this study, an automatic classification method for IPCL patterns based on the JES classification was proposed to estimate the invasion depth of a tumor by extracting microvessels from the magnifying endoscopy images of SESCCs. The microvessels were extracted using the features of IPCLs in the endoscopy images, and the SVM algorithm and proposed thickness measurement method were applied to the extracted microvessels to classify them into the B1, B2, and B3 types satisfying the JES classification. The SVM algorithm employed for classification into the B1 and non-B1 types was trained using a linear kernel. To evaluate the performance of the proposed method, experiments were conducted with 114 magnifying endoscopy images of SESCCs. The results showed an accuracy of 83.3%, with a sensitivity and specificity of 74.5% and 89.6%, respectively. Furthermore, the microvessel thickness was measured and compared to facilitate the classification of the non-B1 type into the B2 and B3 types. When the cut-off ratio of the largest thickness of the representative microvessels to the average thickness of five B2-type microvessels was set to 3.7, the experiment results showed an accuracy of 73.1% with a sensitivity and specificity of 73.7% and 72.9%, respectively. This indicated that the proposed method can provide an objective interpretation of the magnifying endoscopy images of SESCCs. Thus, this study demonstrates the feasibility of the proposed method as a computer-aided diagnosis system in clinical practice. A recent study on artificial intelligence for the classification of IPCL patterns in the endoscopic diagnosis of SESCCs analyzed 7046 magnifying endoscopy images from 17 patients (10 SESCC, 7 normal) using a convolutional neural network (CNN) [20]. The accuracy, sensitivity, and specificity for abnormal IPCL patterns were 93.7%, 89.3%, and 98%, respectively. Another recent study introduced a computer-aided diagnosis system for the real-time automated diagnosis of precancerous lesions and SESCCs in 6473 narrow-band imaging images [21]. The sensitivity and specificity for diagnosing precancerous lesions and SESCCs were 98.0% and 95.0%, respectively. These outcomes were also observed in video datasets. However, the main output/outcome of these two studies was to only differentiate between the normal and abnormal. Detailed classification into the B1, B2, and B3 types, as presented in this study, was not reported. In addition, the non-B1 type was sub-classified into the B2 and B3 types using the microvessel thickness measurement method developed in this study. This study has several limitations. First, the evaluated dataset had a relatively small number of images (114), and all magnifying endoscopy images were retrospectively obtained from a single center. The age-standardized incidence rate was 5.9 per 100,000 population in 2013, and the proportion of SESCCs in whole esophageal cancers was less than 10% in Korea [22]. Furthermore, only high-quality magnifying endoscopy images were employed. The proposed system might produce erroneous results for images that are blurred or out of focus. Therefore, only a small number of SESCC images were included in this study. Therefore, the ability of the proposed automatic classification method to evaluate poor-quality endoscopy images should be investigated after validating its performance on a large dataset of high-quality images. Despite these limitations, the results demonstrated the ability of the proposed machine learning based computer-aided diagnostic system to obtain objective data by analyzing the pattern and caliber of the microvessels with acceptable performance. Future work will focus on a large-scale, multi-center study to validate the diagnostic ability of the proposed system to predict the invasion depth of the tumor in SESCCs. Furthermore, to stabilize the performance of the proposed system and validate its clinical efficacy, further research is necessary to enhance the processing speed and improve diagnostic consistency