Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the identification of polyps. In this paper, a new multimodal embeddable method for polyp detection and classification in wireless capsule endoscopic images was developed and tested. The multimodal wireless capsule used both 2D and 3D data to identify possible polyps and to deliver cancerous information of the polyps based on 3D geometric features. Possible polyps within the image (2D) were extracted using simple geometric shape features and, in a second step, the candidate regions of interest (ROI) were evaluated with a boosting-based method using textural features. Once the 2D identification of polyps has been performed, the two-class ("malignant" or "begnin") classification of the polyps is achieved using the 3D parameters computed from the preselected ROI using an active stereo vision system. At this stage, a Support Vector Machine (SVM) classifier is used to proceed to the final classification and to make possible a pre diagnosis. The new proposed multimodal approach based on 2D - 3D feature extraction improves WCE capabilities to identify and classify polyps: The boosting-based polyp classification demonstrated a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8% on a database composed of 300 hundred positive examples and 1200 negative ones; Considering the 3D performance, a large scale demonstrator was evaluated and tested to perform in vitro experiments on an ad hoc polyp database. The performance of the 3D approach achieved a correct classification rate (malignant or benin) of approximately 95%.