Mammary gland molybdenum target image automatic classification method based on deep learning

Abstract

The invention discloses a mammary gland molybdenum target image automatic classification method based on deep learning. The method comprises the following steps that step one, square image blocks are selected from the cancerous area and the normal area of a mammary gland molybdenum target image by using different sizes of sliding windows, and a training sample set and a test sample set corresponding to each size are constructed for different sizes of image blocks; step two, a convolutional neural network model corresponding to each size is established, and the model is trained by using the training sample set for each size; step three, the accuracy rate of the corresponding convolutional neural network model is tested by using the test sample set for each size, and the convolutional neural network model for the size corresponding to the highest accuracy rate is selected; step four, the overall connection layer characteristics are extracted by using the selected convolutional neural network model; and step five, the extracted characteristics are inputted to a linear SVM classifier for classification so that the classification types of the image blocks are obtained. The overall connection layer characteristics in the convolutional neural network model are extracted to act as the classification characteristics of the image blocks so that the classification speed and accuracy can be enhanced.

Description

technical field [0001] The invention relates to an automatic classification method for mammography images based on deep learning and a linear classifier, and belongs to the technical field of image information processing. Background technique [0002] At present, mammography images play an important role in the initial screening of breast tumors. In the mammography image, the shape of the tumor area is not the same, there is an irregular circular shape, and there is also a radial shape. Distinguishing normal areas from tumor areas is the key to tumor diagnosis. The traditional method is to rely on pathologists to manually screen, which not only has a heavy workload, but also has great inconsistency in the evaluation criteria of each doctor. How to find an effective descriptor (feature) to describe the difference between the normal area and the tumor area is an urgent problem in the field of tumor diagnosis. Contents of the invention [0003] The purpose of the present in...

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Problems solved by technology

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art, provide an automatic classification method for mammography images based on deep learning, and solve the technical problems of low manual classification efficiency and poor precision in the prior art

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