"Advancing Cancer Diagnostics with Hybrid AI Models: Integrating Multi-Modality Imaging (CT, MRI, PET) for Real-Time, Precision Detection and Personalized Treatment Pathways"

Abstract

The healthcare industry faces substantial problems when trying to find and 
identify cancer patients at an early stage. Medical imaging progressed as 
multi-modality technologies including CT, MRI, and PET scans now help 
doctors locate and keep track of cancer more effectively. Finding and 
recognizing cancer accurately represents the biggest medical challenge 
healthcare faces right now. Multi-scanning platforms enhance doctor 
capabilities to see and track cancer development in patients. Doctors have 
problems making sense of medical image data they receive today. Our 
research brings different artificial intelligence systems together to 
understand cancer through complete imaging datasets. These AI systems 
increase system performance by using deep learning and image fusion 
methods alongside segmentation to make better cancer diagnosis and early 
disease spotting along with predicting cancer evolution. This paper reviews 
the technical obstacles of AI for cancer imaging analysis and evaluates 
ethical concerns before showing where AI technology may evolve in the 
field.