Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Abstract

Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.