[논문]송아지 질병 결정 지원 모델

최동운; 강윤정

doi:10.6109/jkiice.2022.26.10.1462

송아지 질병 결정 지원 모델
A Calf Disease Decision Support Model 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.26 no.10, 2022년, pp.1462 - 1468

최동운 (Department of Computer Engineering, Wonkwang University) , 강윤정 (Division of Liberal Arts, Wonkwang University)

초록
AI-Helper

송아지 질병 진단을 위해 사용되는 여러 데이터 중에서 분변은 질병 진단의 중요한 역할을 한다. 송아지 분변 이미지에서 형태, 색상, 질감으로 건강 상태를 알 수 있다. 건강 상태를 파악할 수 있는 분변 이미지는 분변 상태에 따라 정상 송아지 207개와 설사증 송아지 158개의 데이터를 전처리하여 사용하였다. 본 논문에서는 수집된 송아지 데이터 중에서 분변 변수의 이미지를 탐지하고 합성곱 네트워크 기술을 활용하여 질병 증상을 포함하고 있는 데이터 세트에 대해 CNN과 GLCM의 속성을 결합한 GLCM-CNN을 적용하여 이미지를 학습시켰다. CNN의 89.9% 정확도와 GLCM-CNN는 91.7%의 정확도를 보이는 GLCM-CNN는 1.8%의 높은 정확도를 나타내는 유의미한 차이가 있었다.

Abstract ▼ AI-Helper

Among the data used for the diagnosis of calf disease, feces play an important role in disease diagnosis. In the image of calf feces, the health status can be known by the shape, color, and texture. For the fecal image that can identify the health status, data of 207 normal calves and 158 calves with diarrhea were pre-processed according to fecal status and used. In this paper, images of fecal variables are detected among the collected calf data and images are trained by applying GLCM-CNN, which combines the properties of CNN and GLCM, on a dataset containing disease symptoms using convolutional network technology. There was a significant difference between CNN's 89.9% accuracy and GLCM-CNN, which showed 91.7% accuracy, and GLCM-CNN showed a high accuracy of 1.8%.

주제어

표/그림 (8)

표 Table. 1 Fecal scores 0 to 3 in a 1 increment scale
표 Table. 2 The clinical sign scoring criteria
그림 Fig. 1 structure of GLCM-CNN
그림 Fig. 1 Heatmap of feature extracted image
표 Table. 3 Example of cnn and glcm-cnn images in input layer
표 Table. 4 Data collection for calf parameters.
표 Table. 5 Classification accuracy of glcm-cnn
표 Table. 6 Classification CA, PPV, sensitivity, F1 of glcm-cnn

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