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Oral cancer, which is also called mouth cancer, is cancer of the lining of the mouth, lips, or upper throat that has appeared in more than 355,000 people worldwide and caused more than 177,000 deaths, so it is essential to diagnose it as early as possible. Computed tomography (CT) scan is conducive to oral cancer diagnosis, but classifying oral CT images to cancer and cyst manually is difficult and time-consuming. A novel intelligent model based on improved Inception-v3 for classifying oral cancer and cyst CT images automatically is proposed in this paper. We replace the conventional convolution block in Inception-v3 with the Inverted Bottleneck Block and introduce Squeeze-and-Excitation Block (SEB) and Convolutional Block Attention Block (CBAB). The proposed model in this paper is trained on a dataset consisting of CT images of two classes (oral cancer and cyst), and the proposed model achieves 84.053% accuracy, 82.364% sensitivity, 84.508% specificity for oral cancer classification and outperforms other common models in classifying oral CT images.