Traumatic brain injury (TBI) can result from either a minor bump on the head or significant damage to the brain. MRIs and CT scans are the standard medical procedures, which is unnecessary in the event of a negative result. This paper proposes deep learning approaches for classifying patients into CT positive and CT negative categories and the effects of EEG segmentation for patients with mild TBI (mTBI). The proposed algorithms are convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) to perform classification. In the proposed architectures, preprocessing of EEG is not performed. The proposed architectures of CNN, LSTM, and GRU resulted in high-performing accuracy of \(96 \pm 0.3 \%\) , \(96.74 \pm 0.30 \% \) , and \(97 \pm 0.20\% \) , respectively. The segments with a length of 120 fared better than other segments, according to the results of the various segments. Furthermore, our research shows that the models performed better when the EEG data segments were shorter. This suggests that the models could be applied in real-time scenarios to produce faster outcomes, especially in emergency rooms.