Feature data mining and neural network-based tumor classification method

Abstract

The invention discloses a feature data mining and neural network-based tumor classification method. The method includes the following steps that: the man-made scoring data of the effective lesion features of a tumor ultrasonic image are selected as an original feature data set; a bi-clustering algorithm is adopted to acquire effective local diagnosis modes from the original training data set; features of higher levels are extracted according to the effective local diagnosis modes, so that new feature vectors are formed; with the new feature vectors adopted as the input of a neural network, training is carried out, so that an effective multi-class classifier can be obtained; and feature vectors are extracted from a test sample by using the same manner mentioned above, and the multi-class classifier which is obtained through training is utilized to classify the feature vectors, and the specific classification result of a tumor can be obtained. With the feature data mining and neural network-based tumor classification method adopted, the defect that a traditional computer-assisted method is limited to low-level image features can be eliminated; and higher-level effective features are mined from a large number of man-made scoring feature data sets, and the classifier which can identify many types of tumors can be obtained through training by using a popular neural network classification method.

Description

technical field [0001] The invention relates to the field of ultrasonic tumor picture recognition and diagnosis, in particular to a tumor classification method based on feature data mining and neural network. Background technique [0002] In recent decades, the incidence of cancer has been increasing year by year. From a global perspective, cancer has become the leading cause of death among residents, and millions of people die of cancer every year. Cancer is a serious threat to people's life and health, and its treatment and prevention have also become the focus of scientists in the global medical and related fields. Clinical studies have shown that early and accurate classification of tumor diseases is the key to treating tumors. Accurate classification not only helps to improve the cure rate of tumor patients, but also causes fewer side effects to patients. At present, ultrasound imaging has gradually become a commonly used imaging technology in clinical medicine due to...

AI Technical Summary

Problems solved by technology

These methods have some limitations: (1) The low-level features calculated from image pixel grayscale and texture are used for classification, which are quite different from the high-level semantic features that doctors describe and judge tumors clinically. Understand the diagnostic results; (2) The steps are too complicated, and the accuracy of numerical features depends heavily on image quality, preprocessing, and image segmentation effects; (3) It only involves the classification of benign and malignant tumors, and cannot give more specific classifications As a result, assisting doctors to follow up with precise treatment

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