Research Article Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model Hirald Dwaraka Praveena, 1 Nirmala S. Guptha, 2 Afsaneh Kazemzadeh, 3 B. D. Parameshachari , 4 and K. L. Hemalatha 5 1 Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupati 517102, Andhra Pradesh, India 2 Department of CSE-Artificial Intelligence, Sri Venkateshwara College of Engineering, Bengaluru 562157, India 3 Shabakeh Pardaz Azarbaijan, Tabriz, Iran 4 Department of Telecommunication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570016, India 5 Department of ISE, Sri Krishna Institute of Technology, Bengaluru 560090, India Correspondence should be addressed to B. D. Parameshachari; paramesh@gsss.edu.in Received 29 January 2022; Revised 28 February 2022; Accepted 8 March 2022; Published 26 March 2022 Academic Editor: Alireza Souri Copyright © 2022 Hirald Dwaraka Praveena et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using his- togram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. e obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. e proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network. 1. Introduction In recent times, medical imaging plays a crucial role in early treatment, diagnosis, and detection of several diseases [1]. Recently, medical imaging comprises of dissimilar imaging modalities such as ultrasound, fluoroscopy, computed to- mography, and histopathology that helps in interpreting and understanding the dissimilar organs of the human body [2, 3]. In recent scenario, the medical facilities and hospitals create an enormous number of medical images, where it is a complex task to interpret medical images that needs extensive knowledge [4, 5]. So, researchers developed many support systems such as computer aided diagnosis system and content-based medical image retrieval (CBMIR), to assist radiologists or clinicians for interpreting the medical images [6, 7]. Among the available support systems, CBMIR system gained more attention among the researchers, which aids clinicians in finding the identical medical images during diagnosis [8]. Most of the developed CBMIR systems work based on image information such as edges, texture, color, and shape features are generally extracted from handcrafted feature extraction techniques [9–20]. Incompatibility Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 3297316, 9 pages https://doi.org/10.1155/2022/3297316