A Method for Detection and Classification of Lesion Areas in Clinical Images

Abstract

The invention discloses a method for detecting and classifying diseased areas in clinical images, which includes preprocessing the original clinical images to obtain a global image data set; inputting the obtained global image data set into a convolutional neural network based on the detection of target areas in the image Train in the network model to detect the possible lesion area in the image; frame the detected possible lesion area with a rectangular frame, cut the rectangular frame area to obtain the lesion area, and perform normalization and data enhancement on the lesion area obtained after cutting, A local image dataset containing the lesion area is obtained; the global image dataset and the local image dataset containing the lesion area are input as dual modalities into a two-stream convolutional neural network for classification. The method of the invention better utilizes the multimodal information of the clinical image, combines the global information of the original image and the local information of the lesion area image, and further improves the classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of clinical image processing, and in particular relates to a method for detecting and classifying lesion areas in clinical images. Background technique [0002] Tumor is a major killer that endangers life and health, and has a high mortality rate. The clinical manifestations of malignant tumors vary depending on the organ, location, and degree of development. Most malignant tumors have no obvious symptoms in the early stage, and even if they have symptoms, they often have no symptoms. Specific, when specific symptoms appear in patients, the tumor is often already at an advanced stage. A comprehensive analysis of the patient's clinical manifestations and signs is usually performed, combined with laboratory tests, imaging, and cytopathological examinations to make a definite diagnosis, so as to formulate treatment plans and evaluate prognosis. However, there is still a lack of ideal and specific early diagnos...

AI Technical Summary

Problems solved by technology

However, most current deep learning architectures usually only consider single modality, and cannot make full use of the multimodal information of clinical images.

Images