Developing computer-aided diagnosis systems for medical images, particularly Lumbar Spinal Stenosis analysis
Medical imaging analysis research focuses on developing advanced computer-aided diagnosis systems that can automatically analyze medical images to assist healthcare professionals in accurate and timely diagnosis.
This research specifically targets Lumbar Spinal Stenosis analysis, a common condition affecting the lower back, using state-of-the-art deep learning techniques for automated detection and severity assessment.
The developed system aims to reduce diagnosis time, improve accuracy, and provide consistent analysis across different healthcare settings, particularly beneficial in regions with limited access to specialized radiologists.
Salar is developing the deep learning models for spinal stenosis detection, working with medical imaging datasets and collaborating with healthcare professionals for validation.
The research employs advanced image preprocessing techniques, data augmentation strategies, and ensemble learning methods to achieve robust and reliable diagnostic performance.
The developed framework can be extended to other medical imaging applications, contributing to the broader field of AI-assisted healthcare and medical diagnosis.