An Effective Deep Learning Model for Content-Based Gastric Image Retrieval

In this paper, we propose a feature combination, also known as feature fusion, for improving performance in content-based gastric image retrieval (CBGIR). This study provides a CBGIR system that retrieves images by combining ResNet-18 and ResNet-50 information and finally, the Euclidean distance metric is evaluated for similarity measurement. The proposed approach is also compared to different deep learning techniques such as AlexNet, VGGs (VGG-16 & VGG-19), GoogleNet, SqueezeNet, DarkNet-19 models. The proposed method was examined on the KVASIR database with 4000 images and S different classes. We get the optimum results as average precision of 95.44% and average recall of 19.09 on a scale of 20 using the proposed deep learning model and Euclidean distance metric.