Content-based medical image retrieval method using multiple pre-trained convolutional neural networks feature extraction models

Content-based medical image retrieval (CBMIR), a specialized area within content-based image retrieval (CBIR), involves two main stages: feature extraction and retrieval ranking. The feature extraction stage is particularly crucial for developing an effective retrieval system with high performance. Lately, pre-trained deep convolutional neural networks (CNNs) have become the preferred tools for feature extraction due to their excellent performance and versatility, which includes the ability to be re-trained and adapt through transfer learning. Various pre-trained deep CNN models are employed as feature extraction tools in CBMIR systems. Researchers have effectively used many such models either individually or in combined forms by merging feature vectors from several models. In this study, a method using multiple pre-trained deep CNNs for CBMIR is introduced, utilizing two popular models, ResNet-18 and GoogleNet, for extracting features. This method combines the feature vectors from both models in a way that selects the best model for each image based on the highest classification probability during training. The method's effectiveness is assessed using two well-known medical image datasets, Kvasir and PH 2 . The evaluation results show that the proposed method achieved average precision scores of 94.13% for Kvasir and 55.67% for PH 2 at the top 10 cut-offs, surpassing some leading methods in this research area. (c) 2024 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).