Chronological-hybrid optimization enabled deep learning for boundary segmentation and osteoporosis classification using femur bone

ABSTRACT

This paper devises a technique for diagnosing and classifying osteoporosis using the femur bone’s X-ray images. The devised approach uses the following phases: image acquisition, pre-processing, segmentation of femur boundary, measurement of femoral geometry, feature mining, and osteoporosis categorization. Here, femur boundary segmentation is done using the Deep Contour Aware Network (DCAN), which is trained by the Chronological Artificial Ecosystem Optimization (CAEO) algorithm. Then, the femoral geometry is measured and the most relevant features in the image are identified. Further, osteoporosis is classified using the Convolutional Neural Network (CNN) with Transfer Learning (TL), where the CNN is employed with the hyperparameters of the pre-trained model, LeNet. Here, the Chronological Artificial Ecosystem Tasmanian Devil Optimization (CAETDO) algorithm optimizes the classification process. The CAETDO-CNN-TL is examined based on metrics and have the values of 0.910, 0.927, 0.928, and 0.904 for precision, True Negative Rate (TNR), True Positive Rate (TPR), and accuracy, correspondingly.

KEYWORDS:

• Osteoporosis classification
• femur bone
• deep contour aware network
• segmentation
• transfer learning
• Convolutional Neural Network
• feature mining
• LeNet

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Kiran Dhanaji Kale

Kiran Dhanaji Kale is an Assistant Professor in the School of Engineering at the Presidency University, Bangalore in Karnataka, India. He received his master's and bachelor's degree in Electronics and Telecommunication Engineering from Savitribai Phule Pune University. He is a Review Board member, Editorial Board member in various journals, and reviewer across multiple SCI, SCIE, ESCI and Scopus journals. He has teaching experience of more than 10 years in the areas of embedded systems design, real time operating systems and AI-ML Technology. His core teaching and research interests are in the domains of Machine Learning and Deep Learning. His research focuses on applications of Machine Learning and Deep Learning techniques in various sectors such as biomedical, agricultural and automotive.

Bharati Ainapure

Dr. Bharati Ainapure has completed B.E. in Computer Science and Engineering from Karnataka University and M. Tech in Computer Science and Engineering from Vishweshryaya Technological Univeristy, Karnataka, in 2008. She did her Ph.D. from JNTU, Anantapur, India. Currently, she is working as Associate Professor in Computer Engineering Department, Vishwakarma University, Pune, India. She has more than 20 years of experience in teaching and industry and has published more than 30 research papers in renowned international journals and conferences. She has got an Australian patent grant in 2020. Her research interests include Cloud Computing, Parallel Computing and high performance computing.

Sowjanya Nagulapati

Sowjanya Nagulapati received a Master of Technology (M.Tech.) degree in Digital Electronics and Communication Systems from SR Gudlavalleru Engineering College affiliated to Jawaharlal Nehru Technological University, Kakinada in 2016. She received a Bachelor of Technology (B.Tech) degree in Electronics and Communication Engineering from Dhanekula Institute of Engineering and Technology affiliated to Jawaharlal Nehru Technological University, Kakinada in 2014. She worked as an Assistant Professor at several Engineering colleges from December 2016 to September 2019.

Lata Sankpal

Prof. Dr. Lata Sankpal has completed B.E. in Computer Science& Engineering from Government College of Engineering Aurangabad, Maharashtra in 1991 and M. Tech in Computer Science & Engineering from Walchand College of Engineering Sangli in 2001. She did her Ph.D. in Computer Engineering from College of Engineering, Bharati Vidyapeeth Deemed University, Pune, India. Currently, she is working as Associate Professor in Information Technology Department of Pune Vidyarthi Griha's College of Engineering & Technology and G K Pate (Wani) Institute of Management Pune-09, India. She has more than 30 years of experience in teaching and has published more than 10 research papers in renowned international journals and conferences.

Babasaheb Sambhajirao Satpute

Dr. Babasaheb Sambhajirao Satpute has done PhD in Computer Engineering and has vast experience in teaching and research. Currently he is working as an Associate Professor in the Department of Computer Science and Engineering at JAIN (Deemed to be University), Bangalore, India.