Enhanced Medical Imaging-Based Classification Systems for Bone Health

In-depth analysis of bone health classifications-Normal, Osteopenia, and Osteoporosis through medical imaging has been carried out using deep learning. The variances in the definitions of bone mineral density as a health challenge arise out of causing fractures further compounded by reduced mobility. Therefore, the early detection and accurate diagnosis become very paramount as it concerns these complications. This article describes data augmentation techniques to mitigate class imbalance concerns considering the under-sampled Osteopenia subtype eventually creating a balanced dataset for training the model to demonstrate off-the-chart classification performance. The most accurate CNN model is seen to achieve up to $90.76 \%$ after performance augmentation, surpassing even advanced architectures such as ResNet50 with an $88.19 \%$ accuracy score and MobileNetV3 with an $86.92 \%$ accuracy score. Further performance enhancement is effected through the use of class weights and other state-of-the-art regularization methods. This study thus emphasizes importance of balancing techniques and rigorous model selection required in the development of robust tools for diseased diagnosis. Thus, it opens the door for further strides that make early detection and interventions automatic in the healthcare system towards disease status concerning bone health through scalable and interpretable AI-drivenness, extending interpretations within patient imaging diagnoses in the medical field.