다중 레이블 분류를 활용한안면 피부 질환 인식에 관한 연구
A Study on Facial Skin Disease Recognition Using Multi-Label Classification
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초록
최근 안면 피부 미용에 대한 사람들의 관심이 높아짐에 따라 딥 러닝을 활용한 안면 피부 미용을 위한 피부 질환 인식 연구가 진행되고있다. 이러한 연구들은 여드름을 비롯한 다양한 피부 질환을 인식한다. 기존의 연구들은 단일 피부 질환만을 인식하지만, 안면에 발생하는 피부질환은 더 다양하고 복합적으로 발생할 수 있다. 따라서 본 논문에서는 Inception-ResNet V2 모델을 활용하여 다중 레이블 분류 방법으로여드름, 블랙헤드, 주근깨, 검버섯, 일반 피부, 화이트헤드에 관한 복합적인 피부 질환을 인식한다. 사용한 평가 지표 중 정확도는 98.8%, 해밍손실은 0.003을 달성하였고, 단일 클래스별 정밀도, 재현율, F1-점수는 모두 96.6% 이상을 달성하였다.
Abstract
Recently, as people's interest in facial skin beauty has increased, research on skin disease recognition for facial skin beauty is beingconducted by using deep learning. These studies recognized a variety of skin diseases, including acne. Existing studies can recognizeonly the single skin diseases, but skin diseases that occur on the face can enact in a more diverse and complex manner. Therefore,in this paper, complex skin diseases such as acne, blackheads, freckles, age spots, normal skin, and whiteheads are identified using theInception-ResNet V2 deep learning mode with multi-label classification. The accuracy was 98.8%, hamming loss was 0.003, and precision,recall, F1-Score achieved 96.6% or more for each single class.
참고문헌 (13)
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