Research On Diagnosis Of Breast Cancer Based On Computer Vision

Medical images diagnosis is a challenging research topic in the field of computer vision.breast cancer as a malignant tumor that seriously threatens females life,has great value of interdisciplinary research.If the computer becomes a diagnostic tool,it can help reduce the detection error introduced by human doctors,such as insufficient rest,poor mood,etc.,for the early stage of breast cancer,reduce missed diagnosis rate,can greatly improve the survival rate of breast cancer patients.According to the medical procedure for breast cancer diagnosis,computer vision can be introduced into two steps:The first is to be used in mammogram X-ray examination.This study uses convolutional neural network to build a diagnostic network,that is,input a mammogram X-ray medical image,the diagnostic network automatically determines whether the patient has lesions or not,and splits the lesions area for the next biopsy to collect cell tissue for further diagnosis.This part of the experiment needs to complete diagnosing the severity of disease and segment lesions area,so we set up three experimental programs,in the course of the experiment,the use of focal loss overcome the problem of uneven distribution between medical image sample classes,And the target detection algorithm alleviate the imbalance of the number of front and back scenes in medical images.By comparing the experimental results of the three schemes,it is found that the classification of medical images is badly affected by the small proportion of the target area,then the model can not converge,and the target detection finds the approximate location of the lesions area firstly is better than the directly segment the image.The second is applied in the process of diagnosis of puncture biopsy.This study uses integrated learning ideas to combine the feature extract ability of multiple convolutional neural networks,then we construct a benign and malignant tumor diagnostic classification network,providing a common scheme on how to furtherenhance the capacity of deep convolution network.Finally,the accuracy of the pathological image set of the magnification multiples around 90%.