Tumor image focus area prediction analysis method and system and terminal equipment

Abstract

The invention provides a tumor image focus area prediction analysis method and system and terminal equipment, and belongs to the field of deep learning. The method comprises the steps of collection of image data, diagnosis text and medical history data and prediction analysis of a tumor focus area, and specifically comprises the steps of preprocessing the collected data, and extracting image features through a constructed fusion weighted extraction network model; constructing a one-dimensional vector by using the medical history characteristics of a patient, namely age, gender, Karnofsky performance state, apparent tumor growth speed and function deterioration speed, and extracting the medical history characteristics by using a constructed text characteristic extraction network model of a dynamic convolution kernel; and after text features obtained by using the CBOW network model are fused with medical history features and image features, performing focus area prediction analysis through the constructed double-layer weighted prediction analysis network model. The method can significantly improve the classification prediction effect of the tumor image.

Description

technical field [0001] The invention belongs to the field of deep learning, and in particular relates to a method, system and terminal equipment for predicting and analyzing lesion areas of tumor images. Background technique [0002] In recent years, people's illnesses have shown an upward trend, and the main causes of modern people's illnesses are divided into three categories: one is caused by people's psychological factors, endocrine disorders caused by people's gradually increasing work pressure, abnormal mood swings, immune Second, it is caused by physical factors, such as irregular life schedule, frequent staying up late, unhealthy living habits such as smoking and drinking, ionizing radiation caused by nature or industrial production and medicine, etc. The occurrence of malignant tumors; the third is caused by organic chemical factors, such as benzopyrene in tobacco, which has obvious carcinogenic effects. Aflatoxin can cause lung cancer, skin diseases, and liver canc...

AI Technical Summary

Problems solved by technology

However, no matter what kind of network structure is used, it is classified through the single data source of image, but due to the individual differences of patients, it is difficult to judge through the one-way data source, so the current classification prediction effect is not ideal

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