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Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slices that have polyps. We propose a colorectal polyps detection approach which consists of two cascaded stages. In the first stage, a CNN-based model is trained and validated to detect polyps in axial CT slices. To narrow down the effective receptive field of the detector neurons, the colon regions are segmented and then fed into the network instead of the original CT slice. This drastically improves the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, in the second stage, we propose a multi-view network (MVN) that classifies polyp candidates. The proposed MVN classifier is trained using sagittal and coronal views corresponding to the detected axial views. The approach is tested in 50 CTC-annotated cases, and the experimental results confirm that after the classification stage, polyps can be detected with an AUC about 95.27%.