Please use this identifier to cite or link to this item: http://ir.swu.ac.th/jspui/handle/123456789/15521
Title: การจำแนกภาพอวัยวะในระบบทางเดินอาหารจากกล้องแคปซูลไร้สายโดยใช้ โครงข่ายประสาทเทียมคอนโวลูชัน
Other Titles: Digestive Organ Classification from Wireless Capsule Endoscopy Images with Convolutional Neural Network
Authors: นุวีย์ วิวัฒนวัฒนา
สุภัคศจี ปลุกสว่าง
สิปปภาส ทรัพย์สนอง
สุประวีณ์ สร้อยทองเจริญ
Keywords: การจำแนกอวัยวะ
ระบบทางเดินอาหาร
โครงข่ายประสาทเทียมคอนโวลูชัน
Digestive Organ Classification
Wireless Capsule Endoscopy
Convolutional Neural Network
Issue Date: 2563
Publisher: ภาควิชาวิทยาการคอมพิวเตอร์ มหาวิทยาลัยศรีนครินทรวิโรฒ
Abstract: Lesion location is a critical information that must be reported by gastroenterologists when interpreting capsule endoscopy images. There are few studies on how deep learning models can be applied to classify the locations of gastrointestinal tract. Therefore, we aim to create a deep learning model for classifying the locations of gastrointestinal tract (i.e., esophagus, stomach, small bowel, and colon) based on capsule endoscopy images. Dataset of capsule endoscopy images (n = 723,681 images from 174 patients) was divided into training (n = 40,000) and testing (n = 683,674). The images were labeled into 4 organ locations (esophagus, stomach, small bowel, and colon). We applied the deep learning architecture (InceptionResnet V2) to the training dataset. Then, the performance of the trained model was externally validated using the testing dataset. The confusion matrix of the deep learning model, visualizing the performance of our algorithm. The model classified capsule endoscopy images into 4 classes with accuracy of 97.38%, precision of 89.25%, recall (sensitivity) of 94.07%, and f1- score of 94.91%. The average prediction time was 20 milliseconds per image. In addition, we have used ensemble for comparison and the results were slightly better with accuracy of 94.21%, precision of 97.41%, recall (sensitivity) of 94.22%, and f1-score of 95.39%. The deep learning model demonstrated an excellent classification performance, which can be used as a building block for the ultimate goal of creating a fully automated model for interpreting capsule endoscopy images.
URI: http://ir.swu.ac.th/jspui/handle/123456789/15521
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