Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning

TL;DR

This paper presents a deep learning-based medical image diagnosis system that integrates dual-mode image libraries and achieves high accuracy, aiding clinical diagnosis and tumor analysis.

Contribution

It introduces a dual-mode medical image library and a diagnosis method based on deep learning, improving accuracy and clinical applicability.

Findings

AUROC of 0.9985 indicating excellent feature extraction

Recall rate of 0.9814 demonstrating high sensitivity

Accuracy of 0.9833 showing reliable diagnosis

Abstract

This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical image assisted diagnosis method based on the image is proposed. Through the test of various feature extraction methods, the optimal operating characteristic under curve product (AUROC) is 0.9985, the recall rate is 0.9814, and the accuracy is 0.9833. This method can be applied to clinical diagnosis, and it is a practical method. Any outpatient doctor can register quickly through the system, or log in to the platform to upload the image to obtain more accurate images. Through the system, each outpatient physician can quickly register or log in to the platform for image uploading, thus obtaining more accurate images. The segmentation of images can guide doctors in clinical departments. Then the image is analyzed to determine the location and nature of the tumor, so as to make targeted treatment.