Research On MRI Image Computer Adided Diagnosis Technology Of Prostate Base On Deep Learning

Prostate disease is one of the common health problems in middle-aged and elderly men,and the incidence rate of prostate cancer is increasing year by year.Early detection and diagnosis of prostate cancer can effectively prevent its development into advanced metastatic cancer and improve the survival rate of patients.With the development of artificial intelligence,computer-aided diagnosis has become a research hotspot in medical imaging diagnosis,and automatic segmentation and classification of prostate MRI images has important practical significance in improving the efficiency of doctor’s diagnosis.This project studies the key technologies for computer-assisted diagnosis in prostate magnetic resonance imaging.To address the problems of diverse shapes and fuzzy boundaries of prostate cancer lesions in MRI images,this paper proposes a prostate cancer lesion segmentation model that combines a multi-branch structure and self-attention mechanism.Firstly,the multi-branch structure enhances the ability of a single convolutional layer to extract lesion features,and then the multi-head self-attention mechanism models the global information of the feature map,calculates the correlation between each element in the feature map and other elements,and assigns greater weight to the features of the lesion area in the segmentation region to improve segmentation accuracy.To address the differences in signals,lesion shapes,and surrounding tissues in benign and malignant MRI images of the prostate,as well as the difficulty of conventional convolutional neural networks in focusing on and extracting multi-scale features,this paper proposes a classification algorithm based on conditionally parameterized convolution and parameter-free attention combined with an XGBoost classifier.Firstly,this paper dynamically adjusts the convolution kernel parameters through conditionally parameterized convolution to enhance the adaptability and generalization ability of the network to different input images,and then assigns a weight to each feature point in the feature map to guide the network to pay more attention to important features in prostate MRI images.Finally,the features extracted by the network are used as input vectors to construct a classification model in the XGBoost classifier.To address the problem of poor cross-client model generalization due to data heterogeneity and the lack of sufficient data for stable model training in clients with small data volumes,this paper proposes personalized federated learning based on meta-learning.Firstly,meta-learning is deployed in the federated framework to train a well-initialized shared model that can adapt to different clients quickly,and then the shared layers and personalized layers of the model are divided,allowing different clients to share some model parameters while retaining some specific parameters to achieve personalization.This paper studies and verifies segmentation and classification techniques in computer-aided diagnosis based on deep learning,providing diagnostic suggestions for radiologists and having certain clinical significance.