Autoencoder and CNN for Content-based Retrieval of Multimodal Medical Images

Content-Based Medical Image Retrieval (CBMIR) is a widely adopted approach for retrieving related images by the comparison inherent features present in the input image to those stored in the database. However, the domain of CBMIR specific to multiclass medical images faces formidable challenges, primarily stemming from a lack of comprehensive research in this area. Existing methodologies in this field have demonstrated suboptimal performance and propagated misinformation, particularly during the crucial feature extraction process. In response, this investigation seeks to leverage deep learning, a subset of artificial intelligence for the extraction of features and elevate overall performance outcomes. The research focuses on multiclass medical images employing the ImageNet dataset, aiming to rectify the deficiencies observed in previous studies. The utilization of the CNN-based Autoencoder method manifests as a strategic choice to enhance the accuracy of feature extraction, thereby fostering improved retrieval results. In the ImageNet dataset, the results obtained from the proposed CBMIR model demonstrate notable average values for accuracy (95.87%), precision (96.03%) and recall (95.54%). This underscores the efficacy of the CNN-based autoencoder model in achieving good accuracy and underscores its potential as a transformative tool in advancing medical image retrieval.