2.5. Experimental Methods

An experiment was designed to classify upper gastrointestinal landmarks using deep learning based on three datasets. The training, validation, and test datasets comprised 3840, 1280, and 1280 pieces of data, respectively, with a distribution ratio of 6:2:2. The chosen model for the experiment was DenseNet169, which was implemented using TensorFlow, with a batch size of 16, and stochastic gradient descent as the optimizer. Two experiments were conducted within this experimental framework:

The first experiment involved the classification of upper gastrointestinal landmarks using DenseNet169 with various image sizes. Although the prevalent size for commercial WCE is 320 × 320 [59], in previous research, Iqbal et al. and Handa et al. used a size of 128 × 128 in capsule endoscopy [60,61], which was undertaken to enhance accuracy and speed. The analysis covered input images of sizes 128 × 128, 256 × 256, 384 × 384, and 512 × 512. The goal was to align the results with those of previous studies and consider the image quality of current commercialized WCE images [62,63]. The second experiment aimed to evaluate the accuracy of the datasets with five image sizes by incorporating distinct image preprocessing techniques such as the sharpen and detail filters. These experiments collectively explored the impact of various image sizes and preprocessing techniques on the classification of upper gastrointestinal landmarks using deep learning with DenseNet169.