Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques

Abstract

Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusion. It explores various real-time techniques, encompassing technical solutions, AI-based imaging, and image fusion diagnosis across multiple anatomical areas, and electromagnetic needle tracking. To provide a thorough overview, this paper discusses ultrasound image fusion, real-time in vivo cancer diagnosis with different spectroscopic techniques, different real-time optical imaging-based cancer diagnosis techniques, elastography-based cancer diagnosis, cervical cancer detection using neuromorphic architectures, different fluorescence image-based cancer diagnosis techniques, and hyperspectral imaging-based cancer diagnosis. We close by offering a more futuristic overview to solve existing problems in real-time image-based cancer diagnosis.