PURPOSE: To segment the lung tumor on kilo-voltage X-ray radiographic images acquired during treatment toward the markerless lung tumor tracking. METHODS: Per IRB approval, 1150 radiographic images from 80 lung cancer patients were included in the study. We developed a transfer learning deep segmentation net jury committee (TL-DSN-JC) algorithm to segment lung tumors on these images. The proposed models were initialized with the pre-trained VGG-16/19 networks with all but the weights of the connections between the final two layers frozen. A randomized partitioning was applied to train the deep segmentation net. By independently training 12 different deep segmentation nets (DSNs) to form a jury committee (JC), we could determine whether a pixel belonged to the tumor target. Meta-AI Segment Anything Model (SAM) was also tuned to cross-check with our proposed approach. RESULTS: The results predicted by the TL-DSN-JC algorithm were evaluated using precision, recall, F CONCLUSIONS: The experimental results demonstrated that the proposed algorithm outperformed the conventional deep learning techniques, offering a potential tool for markerless tumor motion tracking on projection images.