Wireless Capsule Endoscopy (WCE) offers an important diagnostic instrument for different gastrointestinal diseases. Enhancing the WCE device with real-time image processing capabilities allows to assist specialized physicians in the long and cumbersome process of inspecting the significant amount of data acquired during the examination procedure, providing the first detection of the signs of relevant diseases that require further attention. In this work, we evaluate different state-of-the-art Convolutional Neural Network models for real-time WCE image classification, focusing on lightweight topologies suitable for execution on low-power microcontroller platforms and integration on the WCE device. The selected WCE-SqueezeNet model achieves 98.5% accuracy in the classification of ulcerative colitis, polyps, and esophagitis against healthy samples, allowing classification at a 16 fps rate on the GAP9 multi-core platform, with 61 ms inference time and 30.6 mW average core power consumption.