A REVIEW ON CLASSIFICATION AND RETRIEVAL OF BIOMEDICAL IMAGES USING ARTIFICIAL INTELLIGENCE

Abstract

Image retrieval and classification are the most prominent area of research in computer vision. Nowadays, bounteous medical images are generated through different types of medical imaging modalities in healthcare systems. It is often very difficult for researchers and doctors to access manage and retrieve images easily. The efficient and effective analysis and usage of heterogeneous biomedical images growing rapidly are a tedious task. Content-based image retrieval (CBIR) is one of the most widely used methods for automatic retrieval of images and widely used in medical images. Abundant research articles are published in different domain of applications related to CBIR and classification. The aim of this study is to provide a road map for researchers by exploring the various approaches, techniques, and algorithms used for medical image retrieval and classification. Feature extraction is the main subject for improving the performance of image classification and retrieval. Bag of visual words techniques and deep convolutional neural networks are widely used in content-based medical image retrieval (CBMIR). The state-of-the-art methods presented in this review are well suited to classify and retrieve multimodal medical images for different body organs. The methods include preprocessing of images, feature extraction, classification, and retrieval steps to develop an efficient biomedical image retrieval system. This chapter briefly reviews the various techniques used for biomedical images, and different methods adopted in classification and retrieval are focused.