Lesion image classification method based on convolutional neural network

A technology of convolutional neural network and classification method, applied in the field of classification of lesion images, can solve the problems of cumbersome methods and poor extraction effect, and achieve the effect of improving the clarity of texture and details and improving the classification effect.

Active Publication Date: 2020-07-31
NANJING TUGE HEALTHCARE CO LTD
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a classification method of lesion images based on convolutional neural network to solve the cumbersome method and poor extraction effect caused by the manual extraction method for feature extraction of lesion images in the prior art The problem

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  • Lesion image classification method based on convolutional neural network
  • Lesion image classification method based on convolutional neural network
  • Lesion image classification method based on convolutional neural network

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[0034] The technical solutions of the present invention will be clearly and completely described below through specific embodiments.

[0035] Esophageal cancer is one of the most common clinical malignant tumors, ranking first among digestive tract cancers. With the highest incidence rate in northern my country, there are more men than women, and the age of onset of patients is mostly over 40 years old. Chronic inflammation of the esophagus can also be the cause of this disease. Early esophageal cancer refers to the infiltration of cancer tissue limited to the mucosa and submucosa. Early diagnosis and early surgical treatment of esophageal cancer have a high survival rate and are completely treatable. Esophageal cancer is a common malignant tumor of the digestive system. Its morbidity and mortality rank 8th and 6th among all tumors in the world, respectively. It is the 5th and 4th. Many precancerous lesions and early esophageal cancer have no obvious features under white l...

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Abstract

The invention relates to a focus area image classification method based on a convolutional neural network. The method specifically comprises the following steps: (1) collecting focus images to manufacture an image database, and marking the types of the images in the image database; (2) enhancing an original image in a training sample set by adopting an image enhancement algorithm to improve the texture and detail definition of the image; (3) constructing a lesion classification network, training the classification network by using the training sample set, and determining network parameters toobtain a classification model; and (4) inputting a lesion image to be tested into the lesion classification network to obtain the category to which the lesion belongs, and completing the classification of the lesion image. According to the method, global and color features and texture and detail features are extracted based on O-stream and P-stream models through the lesion classification networkdouble-stream CNN, and the final classification effect is effectively improved.

Description

technical field [0001] The invention relates to the processing field of lesion image classification, in particular to a method for classifying lesion images based on a convolutional neural network. Background technique [0002] In recent years, with the development of science and technology, endoscopic technology has been widely used clinically, which can achieve the purpose of observing the internal organs of the human body with the least damage. However, each endoscopy will generate a large number of data images. In order to detect lesion images, doctors need to spend a lot of time viewing images, and at the same time, missed and false detections may occur due to visual fatigue. Therefore, developing a set of methods for automatic classification of endoscopic lesion images is a key problem that needs to be solved urgently. At present, in the field of automatic detection of endoscopic lesion images, many researchers have adopted traditional machine learning methods, and t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06N3/04G06N3/08G16H50/20
CPCG06N3/08G16H50/20G06V10/44G06V10/56G06N3/045G06F18/2411G06F18/254G06F18/214
Inventor 缪佳温敏立陈阳
Owner NANJING TUGE HEALTHCARE CO LTD
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