Adagrad Optimizer with Compact Parameter Design for Endoscopy Image Classification - Sofyan Pariyasto; Suryani; Vicky Arfeni Warongan; Arini Vika Sari; Wahyu Wijaya Widiyanto (Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, 2025)
Research on CNN Model and Adagrad Optimizer is expected to help identify diseases in the medical world. Especially in the field of image classification in Gastrointestinal endoscopic procedures . The research is specifically for the process of classifying medical images of Diverticulosis, Neoplasm, Peritonitis and Ureters . Previously, there have been quite a lot of studies on CNN and its various optimizers. However, those who have studied the Adagrad optimizer are not too many, especially those discussing the use of minimum parameters. The use of minimum parameters is expected to be one of the contributions of researchers in the fields of computing and medicine. The research was conducted to determine the use of the best parameters and obtain the highest level of accuracy. The research was conducted using minimum epochs starting from epoch 1, epoch 5, and epoch 10. Then the combination process between epoch and the number of convolution layers between 1 and 5 was carried out, resulting in 15 combinations. The test was carried out using 4000 images with 1000 images in each class. From the results of the test, the highest accuracy value was obtained, namely 82.875%. Then the highest average accuracy value was 81.625%.