  Wireless Capsule Endoscopy(WCE) has been widely used for the screening of
gastrointestinal(GI) diseases, especially the small intestine, due to its
advantages of non-invasive and painless imaging of the entire digestive
tract.However, the huge amount of image data captured by WCE makes manual
reading a process that requires a huge amount of tasks and can easily lead to
missed detection and false detection of lesions.Therefore, In this paper, we
propose a \textbf{T}wo-stage \textbf{M}ulti-scale \textbf{F}eature-fusion
learning network(\textbf{TMFNet}) to automatically detect small intestinal
polyps and tumors in WCE image sequences. Specifically, TMFNet consists of
lesion detection network and lesion identification network. Among them, the
former improves the feature extraction module and detection module based on the
traditional Faster R-CNN network, and readjusts the parameters of the anchor in
the region proposal network(RPN) module;the latter combines residual structure
and feature pyramid structure are used to build a small intestinal lesion
recognition network based on feature fusion, for reducing the false positive
rate of the former and improve the overall accuracy.We used 22,335 WCE images
in the experiment, with a total of 123,092 lesion regions used to train the
detection framework of this paper. In the experiment, the detection framework
is trained and tested on the real WCE image dataset provided by the hospital
gastroenterology department. The sensitivity, false positive and accuracy of
the final model on the RPM are 98.81$\%$, 7.43$\%$ and 92.57$\%$,
respectively.Meanwhile,the corresponding results on the lesion images were
98.75$\%$, 5.62$\%$ and 94.39$\%$. The algorithm model proposed in this paper
is obviously superior to other detection algorithms in detection effect and
performance
