Class-Labeling Method for Designing a Deep Neural Network of Capsule Endoscopic Images Using a Lesion-Focused Knowledge Model
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Capsule endoscopy is one of the increasingly demanded diagnostic methods among patients in recent yearsbecause of its ability to observe small intestine difficulties. It is often conducted for 12 to 14 hours, butsignificant frames constitute only 10% of whole frames. Thus, it has been designed to automatically acquiresignificant frames through deep learning. For example, studies to track the position of the capsule (stomach,small intestine, etc.) or to extract lesion-related information (polyps, etc.) have been conducted. However,although grouping or labeling the training images according to similar features can improve the performanceof a learning model, various attributes (such as degree of wrinkles, presence of valves, etc.) are not consideredin conventional approaches. Therefore, we propose a class-labeling method that can be used to design a learningmodel by constructing a knowledge model focused on main lesions defined in standard terminologies forcapsule endoscopy (minimal standard terminology, capsule endoscopy structured terminology). This methodenables the designing of a systematic learning model by labeling detailed classes through differentiation ofsimilar characteristics.
Abstract
Capsule endoscopy is one of the increasingly demanded diagnostic methods among patients in recent yearsbecause of its ability to observe small intestine difficulties. It is often conducted for 12 to 14 hours, butsignificant frames constitute only 10% of whole frames. Thus, it has been designed to automatically acquiresignificant frames through deep learning. For example, studies to track the position of the capsule (stomach,small intestine, etc.) or to extract lesion-related information (polyps, etc.) have been conducted. However,although grouping or labeling the training images according to similar features can improve the performanceof a learning model, various attributes (such as degree of wrinkles, presence of valves, etc.) are not consideredin conventional approaches. Therefore, we propose a class-labeling method that can be used to design a learningmodel by constructing a knowledge model focused on main lesions defined in standard terminologies forcapsule endoscopy (minimal standard terminology, capsule endoscopy structured terminology). This methodenables the designing of a systematic learning model by labeling detailed classes through differentiation ofsimilar characteristics.
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