Capsule endoscope image organ classification method, device and apparatus and storage medium
A technology of capsule endoscopy and classification methods, applied in the field of image recognition, can solve problems such as difficult to quickly locate organs and difficult to classify organs in capsule images, and achieve the effects of reducing computing costs and improving efficiency and accuracy
Active Publication Date: 2021-05-25
CHANGCHUN UNIV
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However, due to the similarity of the characteristics of the digestive tract organs, the small number and variability of capsule endoscopy image samples, it is difficult to provide high-performance capsule image organ classification, which makes it difficult for capsule endoscopists to quickly locate the corresponding organs.
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[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0045] The invention provides a capsule endoscope image organ classification method, such as figure 1 shown, including the following steps:
[0046] S101. Perform image enhancement on the capsule endoscope image sample to generate a capsule image data set;
[0047] It should be noted that in the image enhancement technology, the number and diversity of images are increased through various image transformations. However, different image transformation methods...
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The invention discloses a capsule endoscope image organ classification method device and apparatus and a storage medium, and the method comprises the steps: carrying out image enhancement of a capsule endoscope image sample, and generating a capsule image data set; selecting and modifying a pre-training model for the capsule image data set; carrying out transfer learning training on the modified pre-training model to obtain a capsule image organ classification model; performing organ initial classification on the to-be-classified organ image through the capsule image organ classification model; and correcting the initial classification result by adopting a road sign-based method in combination with the time sequence characteristics of the to-be-classified organ image. The method provided by the invention is capsule endoscope image recognition based on the deep convolutional neural network, mainly comprises four parts of image enhancement, transfer learning, organ classification and classification correction, realizes automatic classification of capsule endoscope image organs, can help doctors to quickly position the organs, and is high in efficiency, low in calculation cost and high in accuracy.
Description
technical field [0001] The invention relates to the field of image recognition, in particular to a capsule endoscope image organ classification method, device, equipment and storage medium. Background technique [0002] Wireless capsule endoscopy is a first-line diagnostic tool for gastrointestinal diseases. Due to the large number of capsule endoscopic images, the inspection work of capsule endoscopists is a difficult task. [0003] Gastrointestinal organs generally include the esophagus, stomach, small intestine, and large intestine. However, due to the similarity of the characteristics of the digestive tract organs, the small number and variability of capsule endoscopy image samples, it is difficult to provide high-performance capsule image organ classification, which makes it difficult for capsule endoscopists to quickly locate the corresponding organs. [0004] Therefore, how to meet the requirements of capsule endoscopic image organ classification with low computing ...
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IPC IPC(8): G06T7/00G06K9/62G16H50/20G06N3/04G06N3/08
CPCG06T7/0012G16H50/20G06N3/08G06T2207/10068G06T2207/20081G06T2207/20084G06T2207/30028G06T2207/30092G06N3/045G06F18/24G06F18/214
Inventor 李丽娜索宝军李念峰宋志强
Owner CHANGCHUN UNIV