Auxiliary film reading system and method for capsule endoscope image

Abstract

The invention discloses an auxiliary film reading system for a capsule endoscope image. A data acquisition module is used for acquiring capsule endoscopic image data of an examined patient; an image position classification module is used for classifying capsule endoscopic images according to different shot parts by using a first convolution neural network (CNN) to obtain image sequences of different shot parts; an image sequence description module is used for carrying out image feature extraction on the image sequences of different shot parts by using a second convolution neural network to obtain feature vector sequences of different alimentary canal part image sequences and for transforming image features in the feature vector sequences into descriptive texts by using a recursive neural network (RNN) to generating an auxiliary diagnosis report. Therefore, the work load of observing an alimentary canal image by a doctor can be reduced and thus the diagnosis efficiency of the doctor can be improved.

Description

technical field [0001] The invention relates to the field of medical equipment, in particular to a capsule endoscope image-aided reading system and method. Background technique [0002] Gastrointestinal diseases, such as gastric cancer, intestinal cancer, acute and chronic gastritis, ulcer disease, etc., are mostly common diseases and frequently-occurring diseases, which pose a great threat to human health. According to the survey data of the National Cancer Registry Center in 2015, digestive tract cancer accounts for 43% of all cancers. The traditional fiber optic endoscope needs to be inserted into the patient's body for observation, which is extremely inconvenient and causes pain to the patient. Capsule endoscopy can examine the entire digestive tract in a painless and non-invasive manner, which is a revolutionary technological breakthrough. Capsule endoscopes will collect about 50,000 images during the detection process, and a large amount of image data makes the docto...

AI Technical Summary

Problems solved by technology

[0005] The above scheme can only provide early warning for suspicious lesion areas, but cannot classify and identify lesions, the effect is single, and it cannot give the location information of the disease

It is not conducive to the doctor's accurate judgment of the lesion

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