Musculoskeletal Abnormality Detection in Medical Imaging Using GnCNNr (Group Normalized Convolutional Neural Networks wi
- PDF / 1,195,518 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 51 Downloads / 167 Views
ORIGINAL RESEARCH
Musculoskeletal Abnormality Detection in Medical Imaging Using GnCNNr (Group Normalized Convolutional Neural Networks with Regularization) Mukul Goyal1 · Rishabh Malik1 · Deepika Kumar1 · Siddhant Rathore1 · Rahul Arora1 Received: 7 July 2020 / Accepted: 18 September 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract Musculoskeletal abnormality detection serves as an advantage to the professionals in the medical domain and also serves as an assistance in the diagnosis as well as the treatment of the abnormalities. This paper mainly focuses on accurately detecting musculoskeletal abnormalities using various deep learning models and techniques. MURA dataset has been used for experimentation. MURA dataset has 14,863 images of finger, wrist, elbow, shoulder, forearm and hand which has been analyzed using deep learning models. In this research paper, authors have proposed GnCNNr model which utilizes group normalization, weight standardization and cyclic learning rate scheduler to enhance the accuracy, precision and other model interpretation metrics. The musculoskeletal abnormality has been detected by using various deep learning models. Accuracy and Cohen Kappa have been taken as the evaluation criteria. The highest accuracy of 85% and Cohen Kappa statistic of 0.698 was achieved by the GnCNNr model in comparison with the conventional deep learning methods like DenseNet, Inception, Inception v2 model. Keywords GnCNNr · Group normalized CNN · Abnormality detection · Machine learning
Introduction The human body is the structure which comprises various cells and organs. The human body is necessary for carrying out the various types of functions necessary for a healthy living. Any abnormality in the human body can This article is part of the topical collection “Deep learning approaches for data analysis: A practical perspective guest edited by D. Jude Hemanth, Lipo Wang and Anastasia Angelopoulou”. * Deepika Kumar [email protected] Mukul Goyal [email protected] Rishabh Malik [email protected] Siddhant Rathore [email protected] Rahul Arora [email protected] 1
Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
cause uneasiness to the human being. An abnormality is any structural change in one or more parts of the body. Any abnormality in the human body can lead to a functional loss or impairment [1]. Abnormalities can be numerical or structural on the basis of chromosomes or first, second and third on the basis of the degree of deformity [2]. Any type or degree of abnormality can be dangerous to physical as well as mental health [3]. Abnormalities can cause aches in several parts of the body, abnormal postures, confused thinking, reduced concentration, extreme mood swings, inability to cope with stress, excessive anger or violence and major changes in eating habits [4]. Traditionally, abnormalities in the human body are analyzed manually by a professional in the medical domain such
Data Loading...
