Zahraa Shams Alden and Oguz Ata, University of Altinbas, Turkey

Medical image analysis is a very risen area of study and the speed and precision necessary in medical image analysis. Deep learning may aid in resolving medical image processing issues including labelled datasets by experts to learn effectively. In the medical field, working with limited access to large volumes of labeled data can present significant challenges. Another challenge is the complexity of medical data. Therefore, this study proposed a deep neural network-based model for medical imaging to detect osteoporosis using transfer learning with MobileNetV2. Class weights are used to alleviate class imbalance, and the learning rate schedule improves model adaptability. The model was created in two variants: one with a learning rate schedule and class weights with an accuracy of 96%, and the second model with only a learning rate schedule with an accuracy of 94%. The anticipated experimental results should illustrate the efficiency of the proposed framework for the future designing of deep learning models for predicting bone fracture and speeding up medical data analysis and interpretation.

Medical image analysis, Machine learning, CNN, Transfer Learning, Osteoporosis, Deep learning, MobileNetV2