Disclosed are a deep convolutional neural network-based breast cancer auxiliary diagnosis method and apparatus, the method comprising the following steps: acquiring a breast cancer CT image, and preprocessing the breast cancer CT image; performing lesion segmentation on the breast cancer CT image by using a medical image segmentation algorithm of a deep convolutional neural network; and feeding back and outputting segmentation results in the form of pictures, and transmitting outputted data to the background for storage. Lesion segmentation is performed on the breast cancer CT image by using the medical image segmentation algorithm of the deep convolutional neural network. Since the foregoing deep convolutional neural network is an algorithm model obtained after training a large number of relevant images, the accuracy of results outputted after the corresponding image is segmented by the algorithm model is higher. Before a doctor makes a diagnosis, preliminary results may be quickly obtained by means of the foregoing algorithm, and then corresponding results are fed back to the doctor in the form of pictures, which provides an important reference for the diagnosis of the doctor, thereby effectively improving the diagnostic efficiency of breast cancer.
DEEP CONVOLUTION NEURAL NETWORK-BASED BREAST CANCER AUXILIARY DIAGNOSIS METHOD AND APPARATUS