In a high-speed moving mobile environment, the channel state information (CSI) in the last part of the packet is different from the actual channel in the beginning part. Therefore, the channel estimation accuracy is degraded, especially when a small number of pilot symbols are used to ensure transmission efficiency. For the above reasons, it is necessary to compensate for CSIs to achieve reliable communication. Decision feedback channel estimation (DFCE) has been widely considered to be one of the channel tracking methods. However, the presence of time and frequency selective fading environments still causes estimation errors due to the decision-making process. We focused on the time-frequency domain response of the CSIs, which can be represented as a two-dimensional image. This paper newly proposes a regression convolutional neural network (CNN) based channel tracking scheme using the time-frequency domain response of the CSIs by DFCE for training and prediction to solve these problems. Computer simulation results demonstrate that the proposed scheme can achieve higher BER performance than the conventional schemes.