Computer-aided diagnosis of Canine Hip Dysplasia using deep learning approach in a novel X-ray image dataset

ABSTRACTCanine Hip Dysplasia (CHD) is a congenital disease with a polygenic hereditary component, characterised by abnormal development of the coxo-femoral joint which results in poor coaptation of the femoral head in the acetabulum; the disease rapidly progresses to osteoarthritis of the hip. While dysplasia has been recognised in practically all canine breeds, it is much more common and of concern in medium and large dog breeds with rapid development. Dysplasia in predisposed breeds, particularly the German Shepherd, is the object of screening based on systematic radiological control in some countries. Our collected dataset comprises 507 X-ray images of dogs affected by hip dysplasia (HD). These images were meticulously evaluated using six Deep Convolutional Neural Network (CNN) models. Following an extensive analysis of the top-performing models, VGG16 emerged as the leader, achieving remarkable accuracy, recall, and precision scores of 98.32%, 98.35%, and 98.44%, respectively. Leveraging deep learning (DL) techniques, this approach excels in diagnosing CHD from hip X-rays with a high degree of accuracy.KEYWORDS: Canine Hip Dysplasia diagnosisdeep learningtransfer learningX-rayimage classification AcknowledgementSpecial thanks to Dr. Samir DJEMAI, a lecturer at the National Veterinary Institute of the University of Constantine, and the DHONDT NUNES veterinary clinic in France for providing the authors with dog hip radiographic images. This work would not have been possible without their invaluable assistance.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsChaouki BoufenarChaouki Boufenar is an Algerian scientist and researcher known for his work in the field of artificial intelligence and data science. He is currently a lecturer at the Computer Science Department of the University of Algiers. He received a Ph.D. in Computer Science from the University of Constantine 2 (Abdelhamid Mehri) in 2018. Chaouki Boufenar has been affiliated with several academic and research institutions, including the University of Paris-Saclay (Laboratoire de Recherche en Informatique), the University of Constantine, and the University of Jijel in Algeria. He has published several research papers and articles in the field of Computer Science and Artificial Intelligence. His areas of interest include data science, deep learning, and computer vision.Tété Elom Mike Norbert LogoviTete Elom Mike Norbert Logovi is currently working as a teaching assistant at Laval University. He is also currently pursuing his M.Sc. degree in Computer Science with a thesis at the same university. He received his Bachelor's degree in Computer Systems from the Department of Computer Science at Benyoucef Benkhedda Algiers 1 University. His research area includes Machine Learning, Deep Learning, and Computer Vision.Djemai SamirDjemai Samir is currently a lecturer and researcher at the Institute of Veterinary Sciences of the University of Constantine, Algeria. He received his Doctor of Veterinary Medicine (DVM) degree from the Institute of Veterinary Sciences of Constantine in 2005, his Magistere degree in Veterinary Medicine from the National Veterinary School of Algiers in 2008, and his Ph.D. in Veterinary Medicine from the Institute of Veterinary Sciences of Constantine in 2017. He also practiced veterinary medicine in a private veterinary practice from 2007 to 2014. As part of his scientific research, he is interested in many scientific areas of veterinary medicine, including veterinary parasitology, carnivore pathology, and avian pathology. He has published several papers in international scientific journals and has presented at several international conferences.Imad Eddine LassakeurImad Eddine Lassakeur is an Algerian computer science researcher currently pursuing a Master's in Computer Science at Laval University in Quebec, Canada. With a background in Computer Science and Intelligent Computer Systems Engineering, he has engaged in a diverse range of research projects, honing his expertise in key areas. Imad's areas of interest encompass artificial intelligence, computer vision, and Natural Language Processing (NLP). Beyond his academic and research pursuits, Imad maintains a profound curiosity for emerging technologies and their potential to transform industries. His multifaceted interests exemplify his commitment to staying at the forefront of technological advancements and his unwavering passion for the field of computer science.