Abstract:
"The study forwards a computer-aided detection application and a deep learning algorithm for detecting brain
defects from CT images, which pave a path to improve the accuracy of the CT image interpretation task
considering both the sensitivity and the specificity of the diagnosis of the defect. The application ensures a more
accurate diagnosis and interface, which is easy to use for medical professionals.
The proposed system leverages YOLOv8, a deep learning algorithm utilised specially for real-time object
detection. The method for detecting brain defects is used in clinical settings and smoothly incorporated into the
current processes of medical imaging. The study demonstrates the viability and efficacy of employing YOLOv8
for automated brain defect diagnosis from CT scans, opening the door for improvements in patient outcomes
and medical imaging technology breakthroughs."
