A mammary gland auxiliary diagnosis system and method based on fusion depth characteristics

Abstract

The invention provides a mammary gland auxiliary diagnosis system and method based on fusion depth characteristics, and relates to the technical field of medical image post-processing. The system comprises a preprocessing unit, a lump detection unit, a fusion depth feature extraction unit and a lump diagnosis unit, and is characterized in that an original mammary gland image is preprocessed, and amammary gland region is divided into a plurality of non-overlapped sub-regions; Mammary gland sub-region depth features are extracted by using a convolutional neural network CNN, and US- ELM is usedto cluster the depth characteristics of each sub-region to obtain mammary gland lumps and non-lump regions; a convolutional neural network CNN is used to extract lump depth features, lump morphology and texture features are extracted, and the lump depth, morphology and texture features are fused into a fused depth feature; And the fusion depth features are learned by using an extreme learning machine (ELM) to finally obtain benign and malignant diagnosis results of the lumps. The method is applied to breast auxiliary diagnosis, and accurate diagnosis of breast diseases can be effectively assisted.

Description

technical field [0001] The present invention relates to the technical field of post-processing of medical images, in particular to a breast auxiliary diagnosis system and method based on fusion depth features. Background technique [0002] Breast cancer seriously endangers women's lives and health, and its morbidity and mortality rank first and second among women's diseases, respectively. Early detection of tumors can effectively reduce breast cancer mortality. Mammography has become the most commonly used detection method for early screening of breast cancer because of its low detection price and sensitivity to small lesions in the breast. However, in the actual diagnosis process, due to the fatigue and inattention of radiologists, the complexity of breast structure and other reasons, the diagnostic accuracy may not be high. In response to these situations, computer-aided diagnosis of breast cancer came into being. [0003] The main process of the classic auxiliary breas...

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Problems solved by technology

The characteristics obtained from the doctor's experience have their advantages, but there must be some characteristics that have not been discovered by the doctor or cannot be expressed

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