Detection of Dirty Bowel Disease Through Palm Image Analysis Using CNN-VGG16 Algorithm - Kurniasari, Calycha; Yustanti, Wiyli (Universitas Negeri Surabaya, 2025)
Early detection of disease is very important in improving the quality of human health. The quality of life of patients suffering from gross bowel disease can be significantly affected, including daily activities, work, and interpersonal relationships. One promising innovative method in the healthcare field is disease detection through palm image analysis. The solution to this problem is done by implementing the Convolutional Neural Network (CNN) algorithm using the VGG16 architecture model which can be operated by uploading palm images to detect Dirty Bowel Disease, Other Diseases (Not Dirty Bowel), and Healthy Hands through a web-based application. Based on the test results, the test accuracy value is 0.4800, F1-Score for the dirty gut disease category is 0.62, F1-Score for Other Diseases (Not Dirty Intestines) is 0.54, F1-Score for the Healthy Hands category is 0.29, and the overall F1-Score is 0.50. The white box test results show that the system can run well in all test scenarios applied. While the black box testing results show that the application functions as expected. In addition, the prediction results using the image import feature are supported by a confidence score with an average value of 48.89% for all three categories.