Investigation of Deep Learning Model for Prediction of Lumbar Spondylolisthesis through X Ray images

Abstract

ABSTRACT newlineDeep Learning (DL) algorithms in the healthcare sector have risen in popularity and attracted academic groups in the last decade. In the healthcare industry, DL algorithms have practically limitless applications. DL models are now being used to help hospitals streamline administrative operations, customize medical treatment, and treat disorders. DL models can significantly enhance the prediction of medical illness, which is beneficial to both doctors and patients. The process of developing a learning-based model for disease prediction saves time and also prevents the disease from becoming chronic. newlineThe interdisciplinary collaboration resulted in the development of novel models to examine difficulties connected to spondylolisthesis (vertebral slippage over another), with good results. Medical images are being used and interpreted in contexts beyond the radiology department. Because manual qualitative readings are not reproducible, a DL-based method is utilized as a compliment. This thesis would be a tremendous resource for modelling and application in medical disease diagnosis. newlineThis thesis addressed the issues with lumbar spondylolisthesis diagnosis that have been repeatedly raised by previous researchers in the domain, but the model proposed in related research is not applicable in real-time. This problem is investigated in this thesis through real-time disease detection which is now possible in a variety of daily scenarios via mobile and web applications. newlineThe goal of this thesis is to automate the diagnosis of lumbar spondylolisthesis through spinal x-ray. This is extremely beneficial because it will help technicians in rural areas in making decisions. The research done in this thesis would allow incredibly quick diagnosis of clinical scans that radiologists and doctors could use in routine clinical practice. However, spondylolisthesis prediction from X-ray images is more challenging because of the different modalities in the image dataset.This thesis summarises four models that really can help in lumba